HEALTH-RELATED QUALITY OF LIFE MEASURE (EQ-5D-5L ... · studies, mahidol univ. thesis / iv...

HEALTH-RELATED QUALITY OF LIFE MEASURE (EQ-5D-5L): MEASUREMENT PROPERTY TESTING

AND ITS PREFERENCE-BASED SCORE IN THAI POPULATION

JUNTANA PATTANAPHESAJ

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR

THE DEGREE OF DOCTOR OF PHILOSOPHY (PHARMACY ADMINISTRATION)

FACULTY OF GRADUATE STUDIES MAHIDOL UNIVERSITY

2014

COPYRIGHT OF MAHIDOL UNIVERSITY

iii

ACKNOWLEDGEMENTS

This project is supported by the Burden of Diseases Project, Thailand; and

the EuroQol foundation, The Netherlands. The Health Intervention and Technology

Assessment Program (HITAP) is supported by the Thailand Research Fund under the

Senior Research Scholar on Health Technology Assessment (RTA5580010) and

ThaiHealth Global Link Initiative Program (TGLIP), supported by ThaiHealth

Promotion Foundation. The findings and opinions in this report have not been endorsed

by the above funding agencies and do not reflect the policy stance of these organizations.

I would like to express my gratitude to all those who gave me the possibility

to complete this thesis. I would like to thank the leader of Health Intervention and

Technology Assessment Program (HITAP), Dr.Yot Teerawattananon for giving me

permission to commence this thesis in the first instance.

I am deeply indebted to Assist Professor Dr. Montarat Thavorncharoensap

(major advisor), Dr.Sirinart Tongsiri (co-advisor) for their guidance, supervision, kindly

suggestions and continual encouragement. Special thanks also to Dr.Lily Ingsrisawang,

Miss Wantanee Kulpeng, Miss Pritaporn Kingkaew for their suggestion on data

analysis.

I am also grateful to Dr.Thunyarat Anothaisintawee, six interviewers, all

field coordinators and all respondents for their kindness and facilitation of the data

collection. Grateful acknowledgement is extended to my class participants of pharmacy

administration program, Mahidol University; Health Intervention and Technology

Assessment Program (HITAP) for their encouragement.

Juntana Pattanaphesaj

Fac. of Grad. Studies, Mahidol Univ. Thesis / iv

HEALTH-RELATED QUALITY OF LIFE MEASURE (EQ-5D-5L): MEASUREMENT

PROPERTY TESTING AND ITS PREFERENCE-BASED SCORE IN THAI POPULATION

JUNTANA PATTANAPHESAJ 5337502 PYPA/D

Ph.D. (PHARMACY ADMINISTRATION)

THESIS ADVISORY COMMITTEE: MONTARAT THAVORNCHAROENSAP, Ph.D.,

YOT TEERAWATTANANON, Ph.D., SIRINART TONGSIRI, Ph.D.

ABSTRACT

This research aimed to develop the population-based preference scores of the EQ-

5D-5L (the 5L), Thai version in an effort to compare the measurement properties of the 5L

with those of the EQ-5D-3L (the 3L), and to compare the results of an economic evaluation

from a 2014 study that used the 5L compared with the 3L.

To elicit population-based preference score, face-to-face interviews using the EQ-VT

protocol was undertaken in 12 provinces across Thailand. A representative sample consisting

of 1,207 recruited individuals was used in a stratified stage sampling and quota sampling of

age and gender. Regarding TTO valuation, 86 health states were grouped into 10 blocks. Each

block contained 10 health conditions. For the Discrete Choice Experiment (DCE) valuation,

196 health states grouped into 28 blocks of 7 pairs of health states were used. For each

participant, the block used for TTO valuation and DCE valuation were randomly selected

through the use of the EQ-VT software application. Regarding the comparison of the

measurement properties, a total of 117 diabetes patients treated with insulin completed a

questionnaire including the 3L, the 5L, and SF-36. Measurement properties were then assessed

in terms of distribution, ceiling effect, convergent validity, discriminative power, test-retest

reliability, and patient preference. The result of economic evaluation using the utility derived

from the 5L was compared with those from the 3L in term of incremental cost-effectiveness

ratio (ICER) and cost-effectiveness acceptability curve (CEAC).

The result of the interview showed no inconsistency among 3,125 possible health

states for the 5L. Random effect model with only primary effects was selected. Mobility had

the greatest impact on preference score. The second best score was 0.968 for state 11112 and

the worst score was -0.283 for worst state (55555). In terms of measurement properties,

evidence supported the convergent validity of both 3L and 5L. However, the 5L showed a

trend towards a slightly lower ceiling effect compared with the 3L (33% vs 29%). It also

showed more promise when compared to the 3L in terms of more discriminatory power, more

reliable index score, and more preferable by respondents. In addition, it was found that the

preference scores derived from the 5L yielded lower ICER and produced less uncertainty than

those derived from the 3L. Thus, the 5L could be recommended as a preferred health-related

quality of life measure in Thailand.

KEY WORDS: HEALTH-RELATED QUALITY OF LIFE / EQ-5D / MEASUREMENT

PROPERTY TESTING / PREFERENCE-BASED SCORE / TARIFF / VALUE SETS

180 pages

Fac. of Grad. Studies, Mahidol Univ. Thesis / v

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180 )�R�

vi

CONTENTS

Page

ACKNOWLEDGEMENTS iii

ABSTRACT (ENGLISH) iv

ABSTRACT (THAI) v

LIST OF TABLES x

LIST OF FIGURES xii

LIST OF ABBREVIATIONS xiv

CHAPTER I INTRODUCTION 1

1.1 Background and rationale 1

1.2 Objectives 3

1.3 Expected outcomes and benefits 4

1.4 Definition of terms 4

CHAPTER II LITERATURE REVIEW 7

2.1 Quality of life 7

2.2 Measurement properties of health status questionnaires 14

2.3 SF-36 questionnaires 19

2.4 EQ-5D questionnaires 20

2.5 Multilevel analysis 30

2.6 Discrete choice experiment and logistic regression 35

CHAPTER III METHODOLOGY 37

Session 3.1 : Development of the Thai population-based preference

scores for the 5L Thai version 37

3.1.1 Study design 37

3.1.2 Study location 37

3.1.3 Study population 38

3.1.4 Selection criteria 38

3.1.5 Data collection method 38

vii

CONTENTS (cont.)

Page

3.1.6 Study procedure 43

3.1.7 Data analysis for TTO valuation 43

3.1.8 Data analysis for DCE valuation 53

Session 3.2: Testing the measurement properties of the Thai version of

the 5L compared to the 3L 55

3.2.1 Study design 55

3.2.2 Study location 55

3.2.3 Study population 55

3.2.4 Selection criteria 55

3.2.5 Data collection method 56

3.2.6 Data analysis 56

Session 3.3: Comparison of economic evaluation results using

preference score derived from the 3L and the 5L 60

CHAPTER IV RESULTS 62

Session 4.1 : Development of the Thai population-based preference

scores for the 5L Thai version 62

4.1.1 Respondent’s characteristics 62

4.1.2 Valuation by TTO 64

4.1.3 Data diagnostic tests 66

4.1.4 Data selection to input in the regression model 68

4.1.5 Testing for the functional form 74

4.1.6 The Thai algorithm and the preference scores 80

4.1.7 Comparing Thai preference score with the interim value

sets from mapping technique 82

4.1.8 Valuation by DCE 83

4.1.9 Country-specific data 85

4.1.10 Qualitative data 86

viii

CONTENTS (cont.)

Page

Session 4.2 : Testing the measurement properties of the Thai version of

the 5L compared to the 3L 90

4.2.1 Characteristics of respondents 90

4.2.2 Distribution and ceiling effect 92

4.2.3 Redistribution 93

4.2.4 Convergent validity 94

4.2.5 Discriminative power 95

4.2.6 Test-retest reliability 96

4.2.7 Coefficient of variation 96

4.2.8 Patient preferences 97

Session 4.3 : Comparison of economic evaluation results using

preference score derived from the 3L and the 5L 97

CHAPTER V DISCUSSIONS 103

CHAPTER VI CONCLUSIONS 114

REFERENCES 115

APPENDICES 125

Appendix A Certificate of ethical consideration 126

Appendix B The example of EQ-VT screen 127

Appendix C EQ-5D-3L Thai version 130

Appendix D EQ-5D-5L Thai version 132

Appendix E Background questions 135

Appendix F TTO Health states included in the EQ-VT 136

Appendix G TTO feedback questions 137

Appendix H DCE pairs included in the EQ-VT 138

Appendix I DCE feedback questions 140

Appendix J Country-specific questions 141

Appendix K Qualitative questions 142

ix

CONTENTS (cont.)

Page

Appendix L Questionnaire for testing measurement property 144

Appendix M SF-36v2 Thai version 150

Appendix N Thai preference score for EQ-5D-5L health states 155

BIOGRAPHY 180

x

LIST OF TABLES

Table Page

2.1 Dimension of health profile measures 10

2.2 Dimension of utilities measures 10

3.1 Magnitude of inconsistent responses of 1 respondent 47

3.2 Variables in the TTO model 48

3.3 Functional forms 49

3.4 Example of more severe health state was preferred 54

3.5 Size of (in) consistent response 57

4.1 Demographic characteristic of respondents 62

4.2 The number of respondents by age, gender, and residential area 63

4.3 Health status of respondents by level of severity 64

4.4 Observed mean values by health state’s profiles 65

4.5 TTO feedback 66

4.6 Number of respondents that met the criteria for low quality data for

TTO valuation 69

4.7 Subgroup classification by low quality data and magnitude of

inconsistency 70

4.8 Mean TTO scores by subgroup 71

4.9 Parameter estimates and the fit statistics by subgroup 73

4.10 Coefficients and fit statistics generated from subgroup 2 by functional

form 76

4.11 Coefficients for main effects of the Thai model 80

4.12 Examples for calculating the Thai preference score for the EQ-5D-5L 81

4.13 Comparing EQ-5D-5L value sets’ parameter between Thai’s and interim

scoring 83

4.14 Number of respondents that met the criteria for low DCE data quality 83

4.15 Coefficients and fit statistics of DCE model 84

xi

LIST OF TABLES (cont.)

Table Page

4.16 DCE feedback 84

4.17 Opinions of the respondents towards philosophy of life 85

4.18 Opinions of the respondents on the most and the least important for each

dimension 89

4.19 Demographic characteristic of respondents 91

4.20 Redistribution pattern of response from the 3L to the 5L 93

4.21 Correlation coefficients between EQ-5D and SF-36v2 dimensions 95

4.22 Shannon index ( ) and Shannon’s Evenness index ( ) of the 3L and the

5L 96 4.23 Test-retest reliability of the 3L and the 5L 96

4.24 Economic evaluation results generated from 3 different value sets 98

xii

LIST OF FIGURES

Figure Page

2.1 Concept of quality of life and health-related quality of life 8

2.2 Visual Analog Scale 11

2.3 Standard Gamble 12

2.4 Time Trade-off method 12

2.5 The lead time TTO 13

2.6 The difference between absolute and relative informativity in a 3-level

system and 5-level system 18

2.7 Scatterplot without median trace, and with median trace 31

3.1 TTO screen for state better than dead 40

3.2 Lead time TTO screen for state worse than dead 40

3.3 DCE screen 41

3.4 Example of utility calculation for TTO valuation 44

3.5 Six criteria used to detect low quality of the data for each respondent 46

3.6 Example of the method used to detect logical inconsistency among 3,125

health states 52

4.1 Scatter plot between TTO value (Y) and level of severity (X) of all

respondents 66

4.2 Spaghetti plot between fitted value (Y) and level of severity (X) of 10

selected respondents 67

4.3 Kernel density estimate of residuals 68

4.4 Probability-probability (P-P) plot of residuals 68

4.5 Magnitude of inconsistency among 1,181 respondents 69

4.6 Mean TTO scores by subgroup 73

4.7 Comparison between actual mean score and predicted score of the model

1-8 using data from subgroup 2 78

4.8 Bland-Altman plots of model 1-8 79

xiii

LIST OF FIGURES (cont.)

Figure Page

4.9 Comparing EQ-5D-5L utility score obtaining from surveying and

mapping 82

4.10 Distribution across severity level of the 3L and 5L dimension 92

4.11 Mean, standard deviation, and coefficient variation of preference score 97

4.12 Cost-effectiveness plane comparing SMBG group to no SMBG group for

DM type 1 & 2 99

4.13 Preference score for comparable health states of the 3L and the 5L 100

4.14 Cost-effectiveness acceptability curve for DM type 1 group 101

4.15 Cost-effectiveness acceptability curve for DM type 2 group 102

xiv

LIST OF ABBREVIATIONS

3L EQ-5D-3L

5L EQ-5D-5L

AD Anxiety/depression

AIC Akaike information criterion

CCC Concordance correlation coefficient

DALY Disability Adjusted Life Year

DCE Discrete choice experiment

DM Diabetes

EA Enumeration areas

EQ-5D EuroQol-5dimensions

EQ-VT EuroQol Group’s Valuation Technology

GDP Gross domestic product

H’ Shannon index

HR-QoL Health-related quality of life

HTA Health Technology Assessment

HUI Health Utilities Index

ICC Intraclass correlation coefficient

ICER Incremental cost-effectiveness ratio

IHRP Institute for the Development of Human Research Protections

J’ Shannon’s Evenness index

MO Mobility

MU-IRB Mahidol University Institutional Review Board

MVH Measurement and valuation in Health

NHP Nottingham Health Profile

NICE National Institution for Clinical Excellence

NLEM National List of Essential Medicine

NSO National Statistical Office

xv

LIST OF ABBREVIATIONS (cont.)

OLS Ordinary least square

PD Pain/discomfort

PPS Probability proportional to size

QALY Quality adjusted life year

QoL Quality of life

QWB Quality of Well-Being

RMSE Root mean square error

SC Self-care

SF-12 The SF-12 Health Survey

SF-36 The Medical Outcomes Study 36-item Short-Form

SG Standard Gamble

SIP Sickness Impact Profile

SRM Standardized response mean

TTO Time trade-off

UA Usual activities

UK The United Kingdom

US The United State of America

USD United States Dollar

VAS Visual analog scale

WHO World Health Organization

WTD Worse than dead

Fac. of Grad. Studies, Mahidol Univ. Ph.D. (Pharmacy Administration) / 1

CHAPTER I

INTRODUCTION

1.1 Background and rationale

Due to scarcity of health resource and increasing high cost of available

health technology, demand for health technology assessment (HTA) evidences is

increasing. Recently, HTA has been a significant tool for evidence-based policy

decision making in Thailand (1, 2). According to the Thai national guidelines of HTA

(3, 4), a cost-utility analysis was recommended as a preferred method for assessing the

cost-effectiveness of health technology. For cost-utility analysis, outcome of

intervention or health technology is measured in terms of quality-adjusted life year

(QALY), which enable comparison across different types of health technology. QALY

is suitable to use as measure of health outcomes as it is the most comprehensive and

encompassing both the quantity and quality of life aspects (5). In other words, QALY

is calculated by the amount of life expectancy multiply by the utility score, which is

varied by each individual's preferences of his/her health status. Utility score can be

ranged from 0 (the worse health state or dead) to 1 (the perfect health). At present,

cost-effectiveness threshold endorsed by the Thai National List of Essential Medicine

(NLEM) Committee is 1.2 Gross National Income (GNI) per QALY or approximately

160,000 Baht (6). Based on the given threshold, it can be interpreted that any

pharmaceutical or technology that produces 1 QALY gained with its costs less or

equal to 160,000 Baht or 1.2 GNI will be considered as cost-effective medicine.

At present, guidelines published by many HTA organizations including the

National Institution for Clinical Excellence (NICE) (7), the US panel on Cost-

effectiveness in Health and Medicine (8), and the Thai national guideline of HTA (9)

has recommended EQ-5D as the preferred instrument for assessing the utility for HTA

studies. The EQ-5D, a widely used general health questionnaire for describing and

valuing health outcome, has been developed since 1980s (10). The first version has

Juntana Pattanaphesaj Introduction / 2

now been translated into more than 150 languages and is used worldwide. This is

because it is easy to response, taking a few minutes to complete, so the respondent can

answer by themselves. It is also suited for use in clinical research, and face-to-face

interviews. The first part of questionnaire contains five dimensions i.e. mobility, self-

care, usual activities, pain/discomfort and anxiety/depression. Each dimension of the

EQ-5D-3L (after this “the 3L”) comprises three levels of impairment namely no

problems (level 1), some/moderate problems (level 2), and extreme problems (level 3),

which generates 243 possible health states. The second part values health status using

visual analog scale (VAS), which is a utility measurement using direct method. It is a

20 centimeter vertical scale designed for self-rated. The scale ranges from 0 to 100,

where 0 means dead or the worst health you can imagine and 100 mean the best health

you can imagine. Nevertheless, it should be noted that utilities score should be

calculated from the first part using country-specific preference score or value sets.

In Thailand, the preference scores for the 3L health states were established

since 2009 by Tongsiri et al (11, 12). In estimating the preference-based score for the

3L, a total of 1,409 Thai respondents were randomly selected for interviewing. A total

of 86 health states, grouped into 12 blocks, were employed in the interview. The utility

was directly elicited by time trade-off (TTO) method using Measurement and

valuation in Health (MVH) protocol. Health state 11112 was the second best health

(preference score = 0.766) while the worst health was 33333 (score = -0.454).

As the 3L is limited to three levels of response categories, a substantial

ceiling effect was observed (13-18). In other word, the respondents who are near

highest possible score can’t show any health improvement. In addition, it has

limitations in measuring small changes, especially in mild conditions (19-22).

Previous studies also found that the 3L appeared to be less sensitive when compared to

the SF-12 or SF-36 (13, 14).

In response to the problems previously mentioned, the 5-level of EQ-5D

(EQ-5D-5L, after this “the 5L”) was developed by a task force of the EuroQol group

in 2005 (19, 20). This version includes five levels of impairment for the existing five

dimensions of the EQ-5D. At present, the 5L has now been translated into more than

113 languages including Thai (23). Several studies (21, 22, 24-30) examining the

measurement properties of the 5L have found that it is a reliable and valid measure.


When comparing the 5L with the 3L, it was found that the 5L had a lower ceiling

effect (22, 24-27, 29, 30) and greater discriminative power with the potential to better

detect the differences between groups (21, 22, 24, 26, 27, 30). In addition, it showed

better face validity (19, 21, 31) and test-retest reliability (24, 27, 29).

To our knowledge, the Thai population-based preference scores for the 5L

have not been developed. In the meantime, an interim value sets generated by the

mapping method between the 3L and 5L index scores has been reported by the

EuroQol group (32). The crosswalk project consisted of 3,691 respondents from 6

countries: Denmark, Scotland, England, Poland, Italy, and the Netherlands. In the

project, the respondents had to complete both the 3L and 5L. The statistical

relationship between these two measures is established through regression methods

and presented as an algorithm or a formula. Then, transitional probabilities from 5L to

3L health state are generated. The 5L value sets are obtained by multiplying the 3L

tariffs with their 243 transition probabilities. However, artificial floor effect is

observed. In addition, when mapping the 5L to the 3L value sets, the mapping method

does not allow the value of worst health state (55555) to be lower than that of 33333

because the range of 5L score was limited within the range of the 3L. In addition,

since transitional probabilities were combined from many countries where the

translation process for the 5L was different, cultural difference may occur thus limited

the validity of the value set estimated. At present, the mapping value sets for the 5L

are available for Thailand as well as the following countries: Denmark, Spain, UK,

US, France, Germany, Japan, the Netherlands, and Zimbabwe (33, 34).

Based on the limitation of the 3L mentioned above and the unavailability

of the Thai population-based preference scores for the 5L, there is a clear need to

develop the population-based preference scores for the 5L Thai version for use in

health technology assessment and also to assess the measurement properties of the 5L

in comparison with the 3L.

1.2 Objectives

The objectives of this study are;

1. to develop the Thai population-based preference scores for the 5L,


2. to test measurement properties of the 5L compared to those of the 3L,

and

3. to compare the results of economic evaluation using utilities derived

from the Thai population-based preferences scores for the5L, which were developed

from this study, with those of the 3L

1.3 Expected outcomes

This study is the first study aims to examine the measurement properties of

the 5L (Thai versions). The measurement properties established and documented from

this study will provide vital evidences to justify the use of such instrument to measure

quality of life among Thai population in the future. More importantly, the population-

based preference score for the 5L generated from this study will allow clinicians and

researcher to calculate utility value, which is crucial information for evaluating effect

of health intervention, monitoring treatment process, as well as conducting HTA

research to support health policy decision making. In addition, the results can be used

for international comparison in order to understand similarities and differences of

health preference across population.

1.4 Definition of terms

Cost-effectiveness acceptability curve

Cost-effectiveness acceptability curve (CEAC) was used to summarize the

information on uncertainty in cost-effectiveness analysis (35). It was drawn by plotting

the proportion of the costs and QALY pairs that were cost-effective for the maximum

acceptable ceiling ratio. The CEAC shows the probability that the intervention is

worth for a given value of the maximum acceptable ceiling ratio.


Cost-effectiveness analysis

Type of economic evaluation that measures consequences or health

outcome in terms of physical units (36).

Cost-utility analysis

Type of economic evaluation that measures consequences or health

outcome in terms of utility unit (e.g. QALYs) rather than in physical units (36).

Economic evaluation

A comparative analysis of alternative technology in terms of their costs

and consequences (36).

Health-related quality of life

The value assigned to duration of life as modified by the impairments,

functional states, perceptions, and social opportunities that are influenced by disease,

injury, treatment, or policy (37).

Health states

The differentiated stages of a lifetime or disease progression which can be

temporary or permanent (38).

Incremental cost-effectiveness ratio (ICER)

The ratio between the increase in cost and the increase in mean

effectiveness which is commonly used to compare interventions (39). Lower ICER

indicates better value for money compared with alternative intervention.

Quality of life

The satisfaction of an individual’s value, goals and needs through the

actualization of their abilities or lifestyle (40).

Reliability

The degree of consistency between two measures of the same thing (41).


Preference-based measure

The preference-based measure is an instrument that define an individual’s

health conditions for using in an economic evaluation. Each possible health state is

associated with an estimate of the value (preference or utility weight) that a surveyed

sample of the general population has attributed to these health states. These

preference-based measures are used for estimation of QALYs (38).

Preference

The umbrella term that describes the overall concept of utility and

preference. For this study, the term ‘preference’ is used to denote a latent tendency to

consider desirable or undesirable toward health state (42). The term preference and

utility were used interchangeably in this study.

Utility

The utility is a cardinal measure of the preference for, or desirability of, a

specific level of health status or specific health outcome. Its scale is an interval or ratio

scale, defined by 2 anchor states or outcomes and their scores, on which utilities are

measured. Often defined by full health = 1.0 and death = 0.0 (43).

Validity

Validity is the degree to which certain inferences can be made from test

scores. Since a single test may have different purposes, there is no single validity

index for a test (41).


CHAPTER II

LITERATURE REVIEW

The chapter consists of 4 parts as follows:

2.1 Quality of life

2.1.1 Concept and definition

2.1.2 HR-QoL measurement

2.2 Measurement properties of health status questionnaires

2.2.1 Reliability

2.2.2 Validity

2.2.3 Discriminatory power

2.3 SF-36 questionnaires

2.4 EQ-5D questionnaires

2.4.1 The Thai version of EQ-5D-3L

2.4.2 Measurement properties of the EQ-5D-3L


2.4.4 Comparing the measurement property between the 3L and 5L

2.4.5 Interim method for mapping EQ-5D-5L to EQ-5D-3L value sets

2.4.6 EQ-VT protocol and health state design for the 5L valuation

2.5 Multilevel analysis

2.6 Discrete choice experiment and logistic regression

2.1 Quality of life

2.1.1 Concept and definition

The consequence of health intervention is usually assessed in term of

clinical outcomes, which mainly provide information to physicians however it does

not interested to patients. Hence, the term ‘quality of life’ is aimed to integrate both

Juntana Pattanaphesaj Literature Review / 8

subjective and objective indicators, a broad range of life domains, and individual

values. Current evidences consistently agreed that quality of life is multidimensional.

It is defined as an overall general well-being that comprises both subjective and

objective assessment of physical, emotional well-being, social, and material together

with the extent of personal development and purposeful activity, all weighted by a

personal value sets (40).

WHO defines quality of life as “the individual’s perception of their position

in life in the context of the culture and value systems in which they live and in relation

to their goals, expectations, standards and concerns”. It is a broad ranging concept

affected in a complex way by the person's physical health, psychological state, level of

independence, social relationships, personal beliefs and their relationship to salient

features of their environment (44). It includes positive well-being, rather than lack of

disease. Regarding “health-related quality of life” (HR-QoL), it is used to specify

“health” and exclude other aspects of life that are not generally considered as “health”

such as income, freedom, and environment (45). Although they may adversely affect

health, these problems are often distant from a health or medical concern. Health-related

quality of life is defined as the value assigned to duration of life as modified by the

impairments, functional states, perceptions, and social opportunities that are influenced

by disease, injury, treatment, or policy (37), as shown in Figure 2.1.

Figure 2.1 Concept of quality of life and health-related quality of life

(adapted from Wilson et al, 1995(46))

HR-QoL Overall QoL

Non medical factors

Biologic / Physiologic

variables

Symptom status

Functional status

General health

perception

Characteristic of environment

Psychological, social and economic support

Characteristic of individual

Motivation, values preferences, belief

e.g. material possession, work life, education, etc


2.1.2 HR-QoL measurement

HR-QoL provides useful information to patients, clinicians, health care

payer, and healthcare administrators. HR-QoL construction includes physical health,

mental health, social health and general health from the individual’s perspective (45).

There are 2 basic approaches of HR-QoL measurement: specific and generic

instruments. The specific instrument focuses on characteristics of health conditions

which are specific to the area of interest. This kind of instrument has more

responsiveness than generic instruments as it includes only important characteristics of

HR-QoL that are applicable to their patients. The instrument may be specific to the

diseases, certain problem, or population studied also.

The generic instruments include health profiles, and preference-based

instrument. Health profiles provide a set of scores of individual domains. The

summary score is derived by averaging across scales or domains. The examples of

health profile instrument are SF-36, Sickness Impact Profile (SIP), and Nottingham

Health Profile (NHP) (47). The dimensions of these instruments are demonstrated in

Table 2.1.

The other type of generic instrument, preference-based measures, is

developed from economic and decision theory. It reflects the strength of preferences

that individual has for treatment process or outcome. The term ‘preference’ is an

umbrella term that describes the overall concept of utility and preference. It is used to

indicate a hidden tendency to consider undesirable or desirable toward health state

(26). Utility refers to the preferences of the individual over process and outcome (37).

In the context of health, utility could be defined as “a cardinal measure of the

preference for a specific health conditions”.

Preference-based instrument offer a summary score known as utility, but it

does not show the domains in which improvement. The key elements of preference-

based instrument is that it includes both preference measurements and relate health

conditions to death. Thus, it can be employed in cost-utility analysis (5, 45). The

examples of utility measure are EQ-5D, Health Utilities Index (HUI) Mark 3, and

Quality of Well-Being (QWB). Dimensions of HR-QoL utility measures are

demonstrated in Table 2.2.


Table 2.1 Dimension of health profile measures (47)

NHP SF-36 SIP

1. Energy level

2. Emotional reactions

3. Physical mobility

4. Pain

5. Social isolation

6. Sleep

1. Physical functioning

2. Role limitations due to

physical problems

3. Bodily pain

4. General health perceptions

5. Vitality

6. Social functioning

7. Role limitations due to

emotional problems

8. Mental health

Physical dimension

1. ambulation

2. mobility

3. body care and movement

Psychosocial dimension

4. communication

5. alertness behaviour

6. emotional behaviour

7. social interaction

Independent categories

8. sleep and rest

9. eating

10. work

11. home management

12. recreation and pastimes

Table 2.2 Dimension of utilities measures (47)

QWB EQ-5D HUI Mark 3

1. Mobility

2. Physical activity

3. Social activity

4. Symptoms/problems

1. Mobility

2. Self-care

3. Usual activity

4. Pain/discomfort

5. Anxiety/depression

1. Vision

2. Hearing

3. Speech

4. Ambulation

5. Dexterity

6. Emotion

7. Cognition

8. Pain

There are 2 approaches to measure utility.

1) Direct measurement

For direct methods, respondents are requested to rate the desirability of

different health conditions. They order their preferences, and making trade-offs


between health conditions and alternatives. The examples of direct measures are

Visual Analogue Scale (VAS), Standard Gamble (SG), and Time Trade-off (TTO).

Visual Analogue Scale (VAS) or another word ‘rating scale’ is a simple

line with defined end points on which respondents are able to indicate their

preferences. The line may be vertical or horizontal, may have interval marked out with

numbers as shown in Figure 2.2. VAS does not offer a choice and has no basis in

economic theory. However, it is still widely used to elicit preferences as it takes less

time and easy administration (48, 49).

Figure 2.2 Visual Analog Scale (10)

10

80

100

90

70

60

50

75

95

85

65

55

45

40

35

30

25

20

15

Full health

5

0

Dead


In Standard Gamble (SG), the respondents were requested to choose an

option between alternative health outcomes in the uncertainty situation. Given that the

individual was in a health state ‘i’. Each individual then has to express his/her

preference by choosing between two alternatives (Figure 2.3). Alternative one is

receiving treatment with 2 possible outcomes which are recover to perfect health with

a probability ‘p’ or dead with probability ‘1-p’. Alternative 2 is to remain in health

state i for certain period. The probability ‘p’ then be changed until the individual

cannot tell the different between these two alternatives. This probability ‘p’ will be

equal to the utility of health state ‘i’ (5).

Figure 2.3 Standard Gamble

Time trade-off (TTO) method was established from SG, and it was also

designed to decrease the difficulties of explaining probabilities to respondents. The

TTO method requests respondent to select between 2 options. For example, the

number of years (x) in full health and the number of years (t) in the valued health state

(state i), as shown in Figure 2.4. Then the respondent will be asked to reduce the

number of year in full health (x), until they are indifferent between 2 alternatives. The

health state’s utility is calculated from x by t (Ui = x/t) (48, 49).

Figure 2.4 Time Trade-off method (50)

Perfect health

Dead

Alternative 1

Alternative 2

p

1-p

Current state

Ui = x / t healthy

Dead

Full health

State i

x t

TIME

VALUE


Using conventional TTO to elicit worse than death health state is

problematic because the trade-offs task for the better than death health state was

different from worse than death health state. The method used to elicit preference of

state worse than death in Measurement and Valuation of Health (MVH) generates

extreme negative values (51). The lead-time TTO has been introduced to measure state

worse than death. The lead-time TTO technic can be applied equally for both better

than death and worse than death health state. This approach introduces ‘lead time’ in

perfect health prior to each of the alternatives (Figure 2.5). This approach can avoids

different valuation methods for better than death and worse than death health state.

To calculate utility from the lead time TTO, the lead time which is full

health state will be minus from both the numerator and the denominator in order to

give a result comparable with the usual TTO. If the lead time is 10 years, Ui = (x-10) /

(20-10), when x is number of year in full health of Life A at the point of indifference.

If x is greater than lead time in Life B, the utility will be positive. If x is less than lead

time in Life B, the utility will be negative.

Life A

Life B

Life A

Life B lead time

Figure 2.5 The lead time TTO (51)

Dark cell = full health, diagonal cell = health state i, white cell = death

Health state i = 10 yr

10 yr

20 yr


Better than dead health state

Worse than dead health state


2) Indirect measurement

For indirect method, individual reports their health state’s preference

through multi-attribute instruments. Then, the answers are calculated by preference

function to obtain the individual’s utility. The preference function have been generated

through population survey study, which direct methods were used to elicit preference

of the possible health state (47). Health Utilities Index (HUI) and EQ-5D is the

example of indirect measures.

2.2 Measurement properties of health status questionnaires

2.2.1 Reliability

Reliability is the degree of consistency between two instruments of the

same thing (41). High reliability is needed for discriminative purpose (52). Reliability

coefficient ranges from 0 to 1. The weighted Cohen’s Kappa coefficient can be

employed to demonstrate reliability for ordinal measures. For continuous data,

intraclass correlation coefficient (ICC) will be derived from the variation in the

population (interindividual variation) divided by the total variation, which is the

interindividual variation plus the intraindividual variation (measurement error).

According to Fleiss’s standards for the strength of agreement for kappa values (53),

Cohen’s weighted kappa (k) was determined as follows: poor reproducibility (k < 0.4);

good reproducibility (0.4 < k < 0.75 ; excellent reproducibility (k > 0.75).

The methods used to test reliability differ in that they consider different

sources of error. For instance, test-retest reliability is used when we concern about

stability, and split-half method is used when we concern internal consistency (41).

The test-retest reliability testing is aimed to test the precision of measures

by administering the same test at a later time. The correlation coefficient between two

scores is used to demonstrate the stability of the individual’s scores (54). The duration

between the two tests should be long enough in order that the respondents can’t

remember the previous answers. However the time period should short enough in

order that the clinical symptoms are not changed. The appropriate period is 1-2 weeks,


however it is possible to be otherwise. At least 50 patients is required for the

assessment of reliability (52).

2.2.2 Validity

Validity is defined as the extent to which certain inferences can be made

from test scores or other measurement (41). The degree of validity is important as it is

useful oversimplification to think of validity as truthfulness. The magnitude and

direction of the relationship between two instruments is used to demonstrate the

validity (54). Pearson product-moment correlation coefficient (r) is a correlation

coefficient for continuous variables, while Spearman’s rank-order correlation (rs) is

employed for ordinal data. The correlation coefficient can range from -1 (perfect

negative relationship) through 0 (no systematic correlation), to +1 (perfect direct

relationship). The strength of correlation is determined as follows: absent (r < 0.20),

weak association (0.2≤ r< 0.35), moderate (0.35≤ r<0.50), and strong (r≥0.50) (55).

Content validity is related to the sufficient content of the test samples that covers the

domain about which inferences are to be made. The numerical expression is rarely

used for content validity. It is rather considered by a thorough inspection of the items.

Two persons may well make judgments about the match of the items to the domain.

Then, agreements of ratings could be calculated (41).

Criterion validity refers to the extent to which scores on a particular

instrument relate to a gold standard. It pertains to the empirical technique of studying

the relationship between the tests scores (predictors) and some independent external

measures (criteria). Some researchers make a distinction between two kinds of

criterion validity: concurrent validity and predictive validity. The only distinction

between these 2 types of validity is the ‘purpose of testing’ and ‘time period’ when the

criterion data are gathered. For concurrent validity, the data are collected at

approximately the same time as the test data. In addition, we are asking whether the

test score can be substitute for some less efficient way of gathering criterion data. For

predictive validity, the data are gathered at a later date and we are concerned with the

usefulness of the test score in predicting some future performance (41, 52). On the

other hand, construct validity refers to the extent to which scores on a particular

instrument relate to other measures which is consistent with theory concerning the

concepts that are being measured (52).


2.2.3 Discriminatory power

The Shannon index was initially developed from the information theory

(56). It normally is employed to measure diversity and richness of information in the

communications industry and the ecosystems. The Shannon index can be applied as a

quantifier of the information content of any classification system and can be used to

compare the informational richness of measures. Also, it could reflect discriminatory

power of the instrument. The Shannon index (H’) is defined as follows (56) :

Where;

H' = the absolute amount of informativity captured

C = the number of possible categories (levels)

pi = ni/N, the proportion of observations in the ith category (i = 1,...,C)

ni = the observed number of scores (responses) in category i

N = the total sample size

The Shannon index reflects the absolute information content and depends

on the number of categories. Whereas Shannon’s Evenness index (J') expresses the

relative informativity of a system or ‘evenness’ of a distribution, regardless the

number of categories. In case of an even distribution, the dimension is being most

efficiently used. This means that the discriminant ability of the level descriptors is

maximal.

Shannon’s Evenness index (J') is defined as follows (56) :

Where;

= log2C



The Shannon index can be calculated by dimension separately or whole

instrument. The following is the example of Shannon index calculation by dimension.

Suppose the EQ-5D-3L Mobility dimension is scored by 10 respondents:

no problems (n = 6), some problems (n = 3) and confined to bed (n = 1). Then, C = 3;

N = 10; Plevel1 = 6/10 = 0.6; Plevel2 = 3/10 = 0.3; Plevel3 = 1/10 = 0.1.

Shannon index for Mobility is calculated as

H’ = –((0.6 log2 0.6) + (0.3 log2 0.3) + (0.1 log20.1))

= 1.30

H’max = log23

= 1.58

J’ = 1.30/1.58

= 0.82.

Figure 2.6 illustrates the difference between H’ and J’. Figure 2.6a, which

is two different instruments and both has 3 levels, shows different distribution of

responses. The left figure is skewed distribution, while the right is a rectangular

distribution. This means that the right instrument is superior in discriminating between

patients and that Shannon index and Shannon’s evenness index reached their

maximum values.

Figure 2.6b illustrates the concept of relative informativity (J’). Both left

and right figure shows skewed distribution. However, the right instrument contains 5

levels of response and levels 2 and 4 are unused. As a result, compared to the left, H' is

equal and J' is lower reflecting the unutilized of the 2 levels added.

Figure 2.6c shows the increase in the value of absolute informativity (H').

Given both 3 and 5 level systems produce equally distributions, the value of J' will be

the same, however H' for the 5-level system is greater because it gives more details for

discriminating between respondents.


a 3-level system 3-level system H’ = 1.34 H’ = 1.58 J’ = 0.84 J’ = 1.00

b 3-level system 5-level system H’ = 1.34 H’ = 1.34 J’ = 0.84 J’ = 0.58

c 3-level system 5-level system H’ = 1.58 H’ = 2.32 J’ = 1.00 J’ = 1.00

Figure 2.6 The difference between absolute and relative informativity (H’ and J’) in a

3-level system and 5-level system

2.2.4 Coefficient of variation

Coefficient of variation (CV), is a measure used to show the extent of

variability in relation with the mean of the sample (57). It is obtained by the ratio of

the standard deviation to the mean. It is often presented as a percentage. The

advantage is that %CV allows comparison to other measure, while standard deviation

is often difficult to interpret or compared with other measure as its value based on the

sample data.


2.3 SF-36 questionnaires

The Medical Outcomes Study 36-item Short-Form (SF-36) Health Survey

is a generic health survey which widely used both in Thailand and international. It has

been translated into more than 50 different languages (58). It contains 36 items,

divided into 8 dimensions, i.e. physical functioning (10 items), role limitation due to

physical problems (4 items), bodily pain (2 items), general health perceptions

(5 items), social functioning (2 items), vitality (4 items), role limitations due to

emotional problems (3 items), general mental health (5 items), and a single item

provided perceived change in health (47). The scoring system is a weighted Likert

scale. The items within the same dimension will be summed and then the summed

score is converted to a scale from 0 – 100 (100 indicating the best health level). The

SF-36 is suitable for the persons aged 14 or over. It can be managed by self-report,

computer, or face-to-face. Thus the SF-36 could be used among both general

population survey and clinic research.

Test-retest reliability study of the SF-36 was undertaken in the UK (59). In

that study, the SF-36 questionnaires were delivered to general populations by postal

survey. The second test among subsample of respondents were two weeks later. One

hundred and eighty seven responded the second test. The scores of the second test

were highly related with the first survey. The correlation coefficient ranged from 0.60

and 0.81. For discriminative validity, the study revealed that the SF-36 demonstrated

the best ability to discriminate between groups compared with other generic

instruments, i.e. EQ-5D, Nottingham Health Profile (NHP) (60).

The Thai version of SF-36 was retranslated using forward-backward

method in order to improve its reliability and validity of previous version (61). From

Thai population survey in 2005 (61), it took about 6 minute for self-complete by Thai

people. A psychometric property testing was undertaken by self-complete

administration in 448 respondents. Convergent validity was evaluated with multitrait

scaling. The correlation coefficient between items and its’ dimension which exceed

0.4 were accounted. The study found that the convergent validity were 96.3%.

Discriminant validity was considered from the correlation coefficient between items

and items of other dimension which over 2 times of standard error (2SE) or 0.092. It

was found that average correlation coefficient was 95%. Cronbach’s alpha coefficients


of all dimensions over 0.7, ranged from 0.72 and 0.86. The rate of missing data was

low (1.2%). Factor analysis yielded pattern for factor correlation of the new version

were comparable to that found in the previous version. It can be concluded that the

retranslated Thai version of SF-36 demonstrated good reliability and validity, except

vitality and role-emotional dimension. Thus, the results should be interpreted with

awareness.

2.4 EQ-5D questionnaires

The EQ-5D, a widely used generic instrument for describing and valuing

health outcome, has been developed since 1980s by the international multidisciplinary

researchers network named “EuroQol group” (10). Alan William was the important

economist who inspired and drove the EuroQol group's task. The group's founder

members came from various disciplines such as health economics, medicine,

sociology and psychology, academia, health care, public health and government. They

came from 4 countries: Finland, Netherlands, Sweden and the UK. However, at the

present, it includes members from North America, Australia, New Zealand, Africa,

and Asia, (62).

The current version of EQ-5D was published, following a 1993

moratorium on modification that has largely held until the present. It has now been

translated into more than 150 languages and is used worldwide including Thai. This is

because it was designed for self-complete so it is simple, and take only 2-3 minutes to

answer all items. The EQ-5D can be used in both clinical research, general population

survey, and face-to-face interviews. To use EQ-5D questionnaire, the researchers

register the study on EuroQol website. The licensing fees will be considered by the

EuroQol executive member based on the information described in the registration

form. The amount of fee is depending on the funding source, type of study, sample

size and number of requested version (63).

EQ-5D comprises 2 parts. The first part of questionnaire includes five

dimensions i.e. mobility, self-care, usual activities, pain/discomfort and

anxiety/depression. These 5 dimensions are basic “common core” of characteristics of

quality of life which a majority of people concern high value (10). Each dimension of


the EQ-5D-3L has three levels of responding namely no problems, some/moderate

problems, and extreme problems. Thus, it generates 243 (35) possible health states.

The second part values health conditions using the direct measurement namely VAS.

It is self-rated health on a 20 centimeters scale. The scale ranges from 0 to 100, where

0 means the worst health conditions that you can imagine and 100 mean the best

health conditions that you can imagine. However, utilities score of respondent is

calculated from the first part using country-specific value sets or tariffs.

As the EQ-5D-3L are limited to three levels of response categories, thus

substantial of ceiling effect was observed (13, 14). The respondents who are near

highest possible score can’t show any health improvement. As a result, it has

limitation in measuring small changes, especially in mild condition. In addition, EQ-

5D-3L appeared to be less sensitive when compared to SF-12 or SF-36 (13, 14).

In response to the problems previously mentioned, 5-level of EQ-5D (EQ-

5D-5L version) which comprises 5 levels of responding, has been developed by a task

force within EuroQol group since 2005 (19, 20). Several studies showed that EQ-5D-

5L had lower ceiling effect and greater power to distinguish the difference between

groups when compared with EQ-5D-3L (21, 22). In addition, it showed greater face

validity, and test-retest reliability also (21). At the present, 118 language versions of

the EQ-5D-5L are available (23).

2.4.1 The Thai version of EQ-5D-3L

In Thailand, the Thai version of EQ-5D-3L has been developed and widely

used. Thai national guideline of HTA has also recommended EQ-5D-3L as the

preferred methods for assessing the utility for health technology assessment (3, 4). The

Thai preference scores for the EQ-5D-3L health states were also established since

2009 by Tongsiri et al (11, 12). In estimating the preference-based score for EQ-5D-

3L, a sample of 1,409 Thai respondents from 17 provinces were interviewed. The

sample size calculation and the random selection were conducted by the National

Statistical Office (NSO), Thailand, using multistage stratified sampling method. The

study design followed standardized method namely Measurement and valuation in

Health (MVH) protocol. A total of 86 health states were arranged into 12 blocks. Each

block contains 11 health states which included two anchor states (11111 and 33333), 3


mild health conditions, 3 moderate health conditions and 3 severe health conditions.

The respondents were asked to rank and score all eleven health states according to

their preference. Then, the utility was directly elicited by time trade-off (TTO)

method. The algorithm was generated as follows:

Thai utility = 1 - 0.202 - (0.121 * mo) - (0.121 * sc) - (0.059 * ua) -

(0.072 * pd) - (0.032 * ad) - (0.190 * m2) - (0.065 * p2) -

(0.046 * a2) - (0.139 * N3)

where

mo = mobility

sc = self-care

ua = usual activities

pd = pain/discomfort

ad = anxiety/depression

The way to replace the variable is as follows:

If the answer is level 1, replace mo, sc, ua, pd, or ad with 0.



This study (11, 12) found that only mobility, pain/discomfort, and

anxiety/depression dimension, if the answer is 3, replace m2, p2, or a2 with 1. Replace

those variables with 0 if the answer is level 1 or 2. For all dimensions, if the level 3 is

responded at least once, replace N3 with 1.

In addition, the study found that the second best state is 11112 (score

0.766), and the worst state is 33333 (score -0.454). However, the current model still

suffers from floor and ceiling effects.


Test-retest reliability

The test-retest reliability of the EQ-5D-3L was undertaken in 20

rheumatoid arthritis patients who reporting no change in their arthritis (64). They were


asked to complete a second set of questionnaires after a 2 week interval. The intraclass

correlation coefficient (ICC) was used to demonstrate test-retest reliability. The study

found that the ICCs for EQ-5Dvas and EQ-5Dutility was 0.85 and 0.78, respectively.

These findings were similar to the study by Dorman et al (65). One-third of 2,253

patients with stroke was randomly sampled for secondly response within

approximately 3 weeks to the first responding. The ICC for overall dimension was

0.86. Thus, reproducibility of EQ-5D-3L was excellent.

The reproducibility of the EQ-5D-3L Thai version was also testing among

Thai people (66). After the first of face-to-face interview, one-fifth of 303 type 2

diabetic patients (n=64) was randomly selected to conduct 1-2 week of test-retest

reliability via telephone. The ICC which was calculated using preference weights from

the UK, US, and Japan was 0.74, 0.74, and 0.78, respectively. This study showed that

the reproducibility of the Thai version of EQ-5D-3L was good. However, if the second

test was administered by face-to-face interview, the test-retest reliability of the Thai

version of EQ-5D-3L may be better than the existing results.

Construct validity

Construct validity of the EQ-5D-3L were tested in general population in

Sheffield by postal survey (13). The people aged 16-74 years were randomly selected.

This study found that preference scores were distributed as expected among variables

(e.g. the sociodemographic variables, the diagnosis of health problems, and health

service received). The professional and managerial groups reported better health than

employee. The person who just received health services had worse health than the

person who did not use health services. The patients with chronic health problems had

poorer scores on the functioning dimension. However, the EQ-5D-3L generated

similar score for a matched sample with and without a diagnosis of chronic physical

problem, even though the differences was as expected.

Construct validity of the Thai version of EQ-5D-3L was also assessed

among Thai people (67). A large cohort (n=4,850) of occupational population was

studied. The respondents completed the Thai EQ-5D-3L and Short-Form 36 version 2

(SF-36v2), which was selected as the gold standard due to its widespread use in

clinical research and its validity among Thai people (68). The construct validity was


considered by testing the relationships between the EQ-5D-3L and SF-36v2. The

construct validity was agreed with expected relationships with SF-36v2 scale and total

scores. For example, the respondents who had problems (answer level 2 or 3) for any

of the EQ-5D dimensions reported lower SF-36v2 scores than the respondents who

reported no problems. Nevertheless, the EQ-5D-3L in Thai version showed a

considerable ceiling effect, with 48.7% of participants having an index score of 1.

According to the study, good construct validity of the EQ-5D-3L Thai version is

confirmed among the occupational population in Thailand.

The discriminative power of the EQ-5D-3L English version was

established by testing the relationship between the EQ-5D-3L and SF-36 (13). The

study was undertaken in general population aged 16-74 years in Sheffield by postal

survey. It was found that the distribution of the EQ-5D-3L responses is more skewed

than the UK SF-36 scores among comparable dimensions. The larger ceiling effect for

the EQ-5D-3L dimensions was confirmed. The percentage of the ceiling of the EQ-

5D-3L was higher than the SF-36 for the functional dimensions. A few categories of

the EQ-5D-3L leaded to skewness of the distributions.

Regarding measurement properties of the 3L, the findings were different

across type of patients. From systematic review (69), which assessing construct

validity and responsiveness of four generic measures in schizophrenia, the 3L gave

mixed evidence. The correlation between the 3L index score and specific disease

ranged from weak to strong. However, the correlation between the 3L dimensions

(anxiety/depression) and symptom or functioning measures was strong. For

responsiveness, the VAS score and health state dimensions (e.g. anxiety/depression)

were responsive to change in patients. While index scores did not respond to changes

in most symptom or functioning measures. Similar results were found in another

systematic review concerned about using generic measures among patients with visual

disorders (70). The performance of the 3L in visual disorders was also mixed. In

patients with age-related macular degeneration (AMD), the 3L was unable to

differentiate between severity levels and did not correlate well with other measures.

However, it performed well in patients with conjunctivitis. In patients with glaucoma,

the 3L distinguished between different levels of severity although it was not always

statistically significant.


On the other hand, a review on the validity and comparative performance

of generic scales in rheumatoid arthritis (RA) (71) demonstrated that the 3L show good

validity and responsiveness for use in RA. Similar results were found in another review

concerned about using the 3L among patients with cardiovascular disease. It was found

that the validity and reliability of the 3L showed fairly strong convergent validity when

assessed by correlations with other HR-QoL measures. Also, the study demonstrated

good discriminatory power in detecting the changing of health status by a given clinical

degree. Nevertheless, the index values demonstrated significant ceiling effects.

Although the 3L demonstrated good reliability, validity, and discriminative

ability among general population. However, it may not appropriate for using in patients

with some specific diseases, for example, visual disorders, due to its performance.


Validity

The investigation of the face validity and content validity of the English

version of the 5L was undertaken in healthy participants and patients with chronic

diseases in the United Kingdom in 2008 (19). The study aimed to assess the ease of

use, comprehension, interpretation, and acceptability of two alternative 5-level

versions: alternative 1 ‘No problems-Minor problems-Moderate problems-Major

problems-Unable to’; alternative 2 ‘No problems-Slight problems-Moderate problems-

Severe problems-Unable to’. The study revealed that the health states based on new

labeling were relatively easy to understand. Regarding response scaling, the

participants preferred alternative 2, which used ‘slight’, ‘moderate’, and ‘severe’ for

the central levels.

The study regarding convergent validity of the 5L were conducted in

cancer patients (22). In this study, the participants completed the generic instruments

(English version of the 3L, the 5L, VAS) and the disease specific instruments

(Functional Assessment of Cancer Therapy - FACT). The participants were also

assessed function by health care personnel using Eastern Cancer Oncology Group

(ECOG). It was found that the 5L demonstrated slightly stronger correlations with

ECOG performance status compared with the 3L for all dimensions of health.


Similarly, there was a stronger correlation between the 5L and FACT. In addition, A

larger ceiling effect, defined as proportion of respondents reporting “no problems” for

all dimensions, was observed in the 3L (17%), as compared with the 5L (11%).

Reliability

Test-retest reliability of the 5L was evaluated among Asian breast cancer

patients (72). The time interval is 30 days after self-administering the baseline

questionnaire and states no change in performance status. The study found that the

ICC for the utility index was 0.81 (95% confident interval = 0.73 – 0.87) which

reflected excellent reproducibility.

2.4.4 Comparing the measurement property between the 3L and 5L

Ceiling effect

The ceiling effect is existed when the respondents who are near highest

possible score can’t show any health improvement. It is measured by the proportion of

respondents reporting “no problems” for all dimensions (11111) (22). The previous

studies consistently reported the decreasing of ceiling effect of the 5L compared with

the 3L. Among general population, the ceiling effect decreased from 44% (the 3L) to

35% (the 5L) in US population (25); and it decreased from 66% (the 3L) to 61% (the

5L) in Korean (29). With regards to patient group, the ceiling effect reduced from 17-

39% in the 3L to 10 – 36% in the 5L (22, 24, 26, 27, 30). It accounted for 3-17%

decreasing.

Considering the ceiling effect of the 5L by dimension, previous studies

found highest ceiling effect (80-97%) in self-care dimension (22, 24, 26, 27, 29, 30);

and the lowest ceiling effect (27-71%) was found in pain/discomfort dimension (24,

27, 29).

Test-retest reliability

The test-retest reliability of index scores was evaluated using the intraclass

correlation coefficient (ICC) and the reliability of each dimension was assessed with

Cohen’s weighted kappa coefficient. The duration of second test of previous studies


ranged from 7 to 21 days (21, 27, 30). It was found that the reliability of index score

was better for the 5L (0.52 for the 3L and 0.69 for the 5L) (21). With regard to

reliability by dimension, the 5L demonstrated similar or slightly better reliability

compared to the 3L (27, 30).

Discriminative power

All previous studies determined discriminative power using Shannon index

(21, 22, 24, 26, 27, 30), the results consistently showed that the 5L was better than the

3L in terms of discriminative power or informativity. However in terms of Shannon

evenness index which reflects the rectangularity of response distribution, the

evidences showed that the 5L was similar or slightly better than the 3L.

2.4.5 Interim method for mapping EQ-5D-5L to EQ-5D-3L value sets

EuroQol group proposed methodology for interim scoring for the 5L in

early 2012 (33). It based on the relationship between responses to the 3L and the 5L

descriptive system. In their model, transitional probabilities from 5L to 3L health state

are generated. Hence, a 3,125 x 243 matrix of transition probabilities was created. The

5L tariffs are obtained by multiplying the 3L tariffs with 243 transition probabilities,

and then subsequently summing of them.

According to EuroQol’s task, 3,691 respondents from 6 countries

(Denmark, England, Poland, Scotland, Italy, and the Netherlands,) completed both 3L

and 5L in order to generate transitional probabilities. Then, the 5L tariffs for the other

countries was calculated (i.e. France, Germany, UK, US, Spain, Japan, the

Netherlands, Thailand, and Zimbabwe) (32). Since transitional probabilities were

combined from many countries, which employed different translation method of the

5L, cultural difference may occur. Another limitation was an artificial floor effect on

5L scores. When mapping 5L to 3L value sets, crosswalk-based approach does not

allow the value of worst health state (55555) to be lower than that of 33333.

2.4.6 EQ-VT protocol and health state design for the 5L valuation

The EQ-VT (EuroQol Group’s Valuation Technology) protocol is

designed by the EuroQol group (73). It provides the methodology used for 5L


valuation study but no analysis procedures are recommended. The content in the EQ-VT

protocol includes interview preparations (for example, training of interviewer, pilot

test, recruiting a representative sample); guideline for EQ-VT software; interviewing;

and health states used for TTO and DCE valuation.

With regards to the respondents, the inclusion criteria were 1) 18+ years

old; 2) able to understand the tasks (as judged by the interviewer); and 3) able to give

informed consent. The exclusion criteria were presence of an acute illness or cognitive

impairment that in the opinion of the interviewer would interfere with the study

requirements.

The EQ-VT software was an offline application used for face-to-face

interviewing. Mobile device or laptop was recommended to be used for collecting the

data. Distinguish roles and download privileges for principal investigator (PI), Data PI,

technical staffs, and interviewer were specified in the protocol. This software needed

interviewer to upload the data to the central server via internet regularly or every day.

The data can be downloaded by PI to check quality of data in order to give feedback to

interviewers in the mean times.

Regarding health states, the EQ-VT protocol recommended 86 health

states used to elicit preference for TTO valuation. They were divided into 10 blocks,

and each block contained 10 health states. The EQ-VT protocol also recommends 196

health states used for DCE valuation. They were grouped into 28 blocks. Each block

contained 7 pairs of health states. After register the respondent, the software will

randomly select TTO block and DCE block for interviewing.

Regarding health state design for the 5L valuation, an experiment design

was used to create the content and structure of the set of EQ-5D health states that is

presented to the respondents (74). Efficiency design was minimizing the number of

states and the number of respondents needed to get significant parameters estimates.

Orthogonality (i.e. attribute levels are independent), minimum overlap (i.e. minimum

overlap of levels for each attribute) and level balance (i.e. levels of each attribute

appear the same number of times) are all design optimization criteria that are used to

minimize the number of respondents that are needed.

For lead-time TTO, the design aimed to maximize the information about

parameters. In order to create an optimal experimental design for lead-time TTO, a


Federov algorithm was used. The optimization criterion was D-optimality, which

seeks to minimize the determinant of the inverse matrix X'X of the design. This

criterion results in maximizing the differential Shannon information content of the

parameter estimates.

For DCE, the "efficient design" algorithm used Monte Carlo simulation to

derive a choice set based on a prespecified utility model. The advantage of this

approach is that it is less restrictive in the optimization process. The parameter

estimates of the main effects DCE model from the 3L pilot were used as priors for the

DCE design algorithm in this study. The efficient design algorithm used to optimize

the DCE was D-optimality criterion as the Federov algorithm.

A multi-national pilot study, namely the 4C study, was an experiment study

for the valuation of EQ-5D-5L which included both lead-time TTO and DCE. It was first

conducted in England, Italy, the Netherlands and Canada. The number of respondents of

was 400 per country, each respondent answered 10 DCE questions and 10 TTO questions.

The design needed 10 blocked as a total of 100 health states need to be valued. The results

of this study demonstrated how the algorithm works and this methodology can be used to

inform on the model specifications. The refined design algorithm and model can

subsequently be used in the official EQ-5D-5L valuation study.

Regarding logical inconsistency, it occurred when a more severe health

state get higher value (75-77). Excluding those logical inconsistencies responses may

lead to more data quality input into the model, while at the same time; it may reduce

the representativeness of the value sets estimated. No gold standard method used to

consider the degree of logical inconsistency, Dolan and Kind (76, 77) offered counting

the number of pairwise of inconsistency. For example, if the values of these states

were ranked: 11111 < 11112 < 11113, then there were 3 pairs inconsistency. However,

in order to compare which health state should be valued more or less than another

state, not all pairs of health states can be considered. An eligible pairs should have 1

compared dimension and the 4 remained dimensions should be the same. For example:

state A 13221 and state B 12221, then state A is logically worse than state B because

the level of state A is equal or worse than state B. Given state C is 22132, it can’t be

compared with state A or B because some levels in state C are worse and some are

better.


2.5 Multilevel analysis

Multilevel analysis is a type of regression model that is particularly

suitable for multilevel data, which is the data with complex pattern of variability, or

longitudinal data. The multilevel analysis may be known in literature under a variety

of names, i.e. hierarchical linear model (HLM), Longitudinal data, also called repeated

measurements in medicine or panel data in the social sciences, arise when the

measures were repeated to the same individuals over time (78, 79). If a multi-stage

sampling design has been employed, multilevel statistical model are always needed.

The use of panel data has increased dramatically since it is possibility to control

unobservable individual specific effect. If unobserved variables exist in the regression

model, OLS model will give bias and inconsistent coefficients (80). Multilevel

analysis differs from the usual multiple regression models in the fact that the equation

defining the multilevel model contains more than one error term as follows (81).

Where;

= the value observed for micro-unit I within macro-unit j

= the residual effect for micro-unit I within macro-unit j

= intercept (group-dependent)

= slope (group-dependent)

The group-dependent coefficients can be split into an average coefficient

and the group-dependent deviation:

Substitution leads to the model

and are level-two residuals, while is level-one residuals. All 3

residuals have means zero, given the values of the explanatory variable X. Thus, is

the average regression coefficient and is the average intercept. The first part


( ) is called fixed effect part of the model. The second part

( ) is called the random part. The term can be regarded as a

random interaction between group and X. This model implies that the groups are

characterized by two random effects: intercept and slope.

Multilevel analysis allows the investigation of heterogeneity across units

both in the overall level of the response and in the development over time (78). This

violates the typical assumptions of ordinary regression models and must be

accommodated to avoid invalid inference. The main statistical model for multilevel

analysis is the hierarchical linear model or random coefficient model which allows

intercepts as well as slopes to vary randomly (81). Another type of multilevel analysis

is random intercept model, which intercepts are allowed to vary and assumes that

slopes are fixed. Consequently, for each individual observation, the scores on the

dependent variable are predicted by the intercept that varies across groups.

2.5.1 Assumption of the multilevel analysis

The assumptions of the multilevel regression model are similar to the

conventional multiple regression: linearity, homoscedasticity, and normal distribution of

the error terms. The testing coefficients can be valid if these assumptions are satisfied.

To examine the linearity between dependent and independent variables, a

scatterplot between all variables of a regression model can be used (79). However,

scatterplot often only show the functional form of a relationship for small sample size.

To deal with larger sample sizes, more information is needed to improve the scatterplot.

The median trace is a tool to make a scatterplot smoother. To construct a median trace,

the values on x-axis were divided into strips and the median of y for each strip is

calculated. Then the medians are connected with straight lines (Figure 2.7).

-1-.

50

.51

Val

ue

5 10 15 20 25Severity

-1-.

50

.51

5 10 15 20 25Severity

Value Median bands

Figure 2.7 scatterplot without median trace (left), and with median trace (right)


Homoscedasticity, which is equal to variances of errors, is an important

assumption for OLS (81). This means that the residuals do not depend on the

explanatory variables. However, heteroscedasticity is a frequently occurring

phenomenon in panel data analysis. The techniques used in the multilevel analysis

allow to relax this assumption and replace it by the weaker assumption that variance

depend linearity or quadratically on explanatory variables. Therefore, the

heteroscedasticity will also be prevalent in many multilevel data analyses. This is

indeed the case. In fact, heteroscedasticity is an explicit part of most multilevel

models. The likelihood ratio test (LR test) can be used to inspect heteroscedasticity.

The null hypothesis is equal variance for errors (homoscedasticity). In case

heteroscedasticity was found, robust standard errors should be employed in the model

estimation.

Inspection of normality of residuals should be employed to find outlying

cases that may have high influence on the results of the statistical analysis (81).

Normality of the error terms (residuals) is needed for valid hypothesis testing. This

assumption assures that the p-values for the t-tests and F-test will be valid. Examining

residuals is a key part of all statistical modeling. Residuals are estimating by

subtracting the observed responses from the predicted responses. The results give

information about the reasonable of the assumptions and the appropriateness of the

model. There are various methods used to test for residual normality, i.e. Shapiro-Wilk

W test, normal probability plot, kernel density plot.

Model specification error or model misspecification generally refers to

errors of omission variables; including an irrelevant variables; and incorrect functional

form (82). For hierarchical linear model, specification test is to select relevant

explanatory variables in the fixed part, and relevant random slope in the random part.

The purpose of model specification is to arrive at a model that describes the observed

data to a satisfactory extent but without unnecessary complications. The consequence

of specification error is incorrect results of model estimation or misleading. The

Hausman specification test is used to differentiate between fixed effects model and

random effects model in panel data (83). The null hypothesis is that the errors are not

correlated with the regressors. If it fails to reject null hypothesis, random effects model

are preferred.


2.5.2 Sampling weights

Surveys are often non-representative of the population of interest. This is

because of oversampling of certain groups and different non response rates (84).

Sampling weights should be applied with the data set. The idea is that applying these

sampling weights in the analysis corrects for the non-representativeness of the data by

giving underrepresented groups more weight and overrepresented groups less weight.

The results from unweighted data are sometimes inconsistent and bias.

2.5.3 Model selection criteria

Model selection is the task of selecting a statistical model from a set of

candidate models. Given candidate models of similar predictive or explanatory power,

the simplest model is likely to be the best choice. According to Hendry and Richard

(85), a model chosen for empirical analysis should satisfy the criteria as follows.

1) Data coherency – the residuals estimated from the regression model

should be random. Otherwise, specification error will be found.

2) Valid conditioning – the explanatory variables should be uncorrelated

with the error term.

3) Parameter constancy – the values of the parameters should be stable.

Otherwise, the prediction will not be reliable.

4) Data admissibility – the prediction made from the model should be

logically possible.

5) Theory consistency – the composition of the model, i.e. intercept,

coefficient, should make good sense.

6) Encompassing – the model should include important variables so that it

is capable to explain their results

Several statistics can be used as diagnostic measures. These criteria were

discussed: 1) cross validation; 2) R2 and adjusted R2; 3) Akaike information criterion

(AIC); 4) Concordance correlation coefficient (CCC); 5) Bland-Altman plot; and 6)

standardized response mean (SRM).

Cross validation is a technique used to validate the model in order to

assess the generalizability of tested data set. It is mainly used when the goal of

http://en.wikipedia.org/wiki/Statistical_model


modelling is prediction, and the researcher needs to estimate the accuracy of a

predictive model in practice. The dataset is split into two independent halves, one half

being used for generating a model, and the other half for testing of effects (81). The

advantage is that the testing data and model specification are separated. So the testing

does not lose their validity because of capitalization on chance.

R2 criterion is a measure of goodness of fit of a regression model (82). It

ranges from 0 to 1. The better fitted model yields higher value or close to 1.

Nevertheless, there are some disadvantage of R2. Firstly, it measures goodness of fit in

the way of how close a predicted Y value to its actual value in the sample. Thus no

guarantee that it will predict precise again for another sample. Secondly, the dependent

variable should be the same when compared two or more R2. Thirdly, when more

variables are added to the model, an R2 always increase. Therefore, just adding more

variables into the model, R2 increases, but it may increase the variance of prediction

error also. Adjusted R2 is a penalty for adding independent variables to increase the R2

value. For comparative purposes, adjusted R2 is a better measure than R2. However,

dependent variables must be the same for the comparison to be valid.

The Akaike’s Information Criterion (AIC) is used for choosing best

predictor subsets in regression and often used for comparing models, which ordinary

statistical tests cannot do. The value of the AIC for a given data set has no meaning.

AIC can be useful when comparing the fit of several models to the same data. The

model with the lower AIC is the better model (model is considered to be closer to the

truth). However, the problem with AIC is that it is difficult to interpret as it does not

have well defined endpoints related to a perfect fit or a lack of fit. The AIC is

calculated from the deviance (which is -2 times the log-likelihood) as: AIC = d + 2p,

where d is deviance and p is the number of estimated parameters (86).

Concordance correlation coefficient (CCC), a goodness-of-fit statistic, is

used to determine whether the observed data significantly deviate from the line of

perfect concordance (87). This coefficient is not only measuring how far each

observation deviates from the line fit to the data (precision), but also how far this line

deviates from the 45° line through the origin (accuracy). The CCC value ranges from -

1 to 1. The CCC equation is

Where;


ρc = the concordance correlation coefficient

ρ = the Pearson correlation coefficient, which measures how far

each observation deviates from the best-fit line, and is a measure

of precision, and

Cb = bias correction factor that measures how far the best-fit line

deviates from the 45° line through the origin, and is a measure of

accuracy.

Bland-Altman plot is a simple graphical method used to show prediction

bias and precision (88). It plots the difference of predicted score and actual mean score

on y-axis against the average of those score for each subject on x-axis. The 95% limits

of agreement are calculated by 1.96 x standard deviation. It is expected that the 95%

of dot (the difference between predicted score and actual mean score) should lie within

the limits of agreement.

The standardized response mean (SRM) is one of several available effect

size indices used to gauge the responsiveness of scales to clinical change (89). There

is no consensus on the method used to determine the magnitude of the difference

between two different scores. The SRM is obtained by dividing the mean score change

by the standard deviation of the change as follows.

SRM = (Meanx1 – Meanx2) / SDchange scores

The SRM can be interpreted as: 0 - 0.19 trivial effect; 0.20 - 0.59 small

effect; 0.60 - 1.19 moderate effect; 1.20 - 1.99 large effect; 2.0 - 3.99 very large effect;

and 4.0 - nearly perfect.

2.6 Discrete choice experiment and logistic regression

A discrete choice experiment (DCE) is a task which respondents have to

choose among a set of alternatives. These alternatives consist of attributes and severity

level selected from a descriptive profile. In general, discrete choice models are usually

derived in a random utility model (RUM) framework in which respondents are


assumed to be utility maximizers. Differently from the classic TTO, DCE just needs

respondents to choose preferred health state from 2 options (90). The health state

chose by the respondents was assumed that it gave them higher utility.

The values obtained from DCE valuation were not directly observed and

have to be calculated from the binary responses, the conditional logit model (clogit)

can be employed to estimate health value (90, 91). This model can be used to analyze

the binary outcome data with one or more predictors, where observations are not

independent but are matched or grouped in some way. The values obtained from the

conditional logit model were on an arbitrary scale which is different from the utility

scale where 0 refers to dead and 1 refers to perfect health. Thus the coefficient

generated from the conditional logit model cannot be used directly to calculate QALY.

The transformation or rescale is needed.

Previous studies (91) have successfully anchored DCE results on the utility

scale. The worst health state (health state 55555) predicted by the lead-time TTO

model was taken to anchor health state 55555 of DCE valuation in order to rescale the

arbitrary scale of the conditional logistic model. Thus, both TTO and DCE model

produced the same index value for the worst health state. Rescale was undertaken by

dividing all coefficients obtained from a conditional logistic model by a scalar which

was calculated as follows (91).

(worst health statedce – 1) / (worst health statetto – 1)

After rescale calculation, the utility decrements for each coefficient of

DCE model were obtained.

The logistic regression allows predicting a discrete outcome from a set of

variables that may be dichotomous, categorical, or continuous (86). It has no

assumptions about normality, linearity, and homogeneity of variance for the

independent variables. Logistic regression can be used to fit and compare models. The

goodness-of-fit tests help to choose the model that does the best job of prediction with

the fewest predictors. The conditional logistic regression is a particular analysis used

to analyze the binary outcome data with one or more predictors, where observations

are not independent but are matched or grouped in some way (92). The criteria for

selection of the model are similar to the topic 2.6.3 mentioned above.


CHAPTER III

METHODOLOGY

Comply with the objectives; there are 3 sessions of methodology as follows.

Session 3.1: Development of the Thai population-based preference scores

for the 5L Thai version

Session 3.2: Testing the measurement properties of the Thai version of the

5L compared to the 3L

Session 3.3: Comparison of economic evaluation results using preference

score derived from the 3L and the 5L

Session 3.1: Development of the Thai population-based preference

scores for the 5L Thai version

3.1.1 Study design

This study was a cross-sectional survey, using the EuroQol Group’s

Valuation Technology (EQ-VT) software generated by the Value Set Working Group

(VSWG) of the EuroQol Group.

3.1.2 Study location

This study was conducted in 12 provinces of Thailand: Bangkok, Sing

Buri, Trat, Suphan Buri, Chiang Mai, Chiang Rai, Sukhothai, Surin, Nong Bua Lam

Phu, Roi Et, Krabi, Nakhon Si Thammarat. The data collection was conducted

between 27 August 2013 and 26 January 2014.

Juntana Pattanaphesaj Methodology / 38

3.1.3 Study population

The study population was general Thai population. The sample size was

1,207 respondents. A representative sample was randomly selected by a stratified

three‐stage sampling method with the collaboration from the National Statistical

Office (NSO), Thailand. Firstly, all 77 provinces in Thailand were stratified into 4

regions and Bangkok: North, Northeast, Central, South and Bangkok. The primary

sampling unit was province. Eleven provinces and Bangkok (total=12) were randomly

selected using systematic sampling. The secondary sampling unit was enumeration

areas (EAs). Each EAs may vary considerably in size of household and population, so

probability proportional to size (PPS) was applied. In our study, 120 EAs was selected

from the total of approximately 120,000 EAs in Thailand. The third stage sampling

unit was individuals aged 18 years or over. Ten participants per EA were selected

using quota sampling by age and sex according to the Thai population structure.

Respondents were identified and contacted by area coordinator prior to interview date,

and all of them agreed to be interviewed.

Replacement of the respondent can be done only in specific situation.

Protocol for replacement is explained as follows; 1) If the eligible participant insistently

refuses the interview, individual who is at the same age and sex identified from the same

EAs will be chosen for replacement; 2) If EAs cannot be reached because of unexpected

bad weather or poor road, the closet area will be chosen for replacement.

3.1.4 Selection criteria

Comply with the EQ-VT protocol, the eligible participants was general

population aged more than 18 years old. They should be able to read Thai and able to

understand the tasks (determined by interviewer). The person who presents an acute

illness, cognitive impairment, drunk, or disabled that in the opinion of the interviewer

would interfere with the study requirement was excluded.

3.1.5 Data collection method

The participants were interviewed in a face-to-face setting. The data was

collected using a touch screen laptop which was installed with the software named

EQ-VT offline version. The version used in this study was translated to Thai language


by translator; and proofed by Thai researcher team (Appendix B). This digital method

could results in more consistent valuation interviews and more systematic responses

within valuation studies and between valuation studies. Six interviewers were

recruited and well-trained prior to work.

The brief processes of data collection using computer device and EQ-VT

offline version are followed.

1) The interviewer logins the program by entering the user name and

password.

2) Each respondent is registered. Only the subject’s identification, first

letter of a name and surname of the respondent are inserted.

3) The interviewer explains all process of the interview to the respondent.

At the same time, the introduction screen is showed (Appendix B).

4) The questionnaires used in the following interview consist of 9 parts as

follows.

i. The Thai version of the EQ-5D-5L (Appendix D)

ii. Visual Analog Scale (EQ VAS) (Appendix D)

iii. Background questions (Appendix E)

iv. Preference elicitation by TTO technique

Regarding health state for TTO method included in the EQ-VT

software, there were 10 blocks of health state (Appendix F). Each

block was contained 10 health states and included 1 anchor state

(55555). The blocks used for interview were randomly selected by

the EQ-VT software. The respondent was asked to imagine two

alternative health states which were described on the screen. For

better than dead health state, two alternative health states were life A

= x years in full health; and life B 10 years in the valued health state.

Then the respondent was asked to reduce the number of year in life

A, until they are indifferent between 2 alternatives (Figure 3.1). For

worse than dead health state, lead time approach was used. The lead

time was 10 years in this study, the two alternative health states was

life A 10+x years in full health; and life B 10 years in full health,

then 10 years in the valued health state. Then the respondent was


asked to reduce the number of year in life A, until they were

indifferent between 2 alternatives (Figure 3.2).

Figure 3.1 TTO screen for state better than dead

Figure 3.2 Lead time TTO screen for state worse than dead

มปญหาในการเดนเลกนอย มปญหาในการอาบน า หหรออใเเออาดยยนนเอเเลกนอย มปญหาในการท ากจกรรมทท าเปนประจ าปานกลาเ มอาการเจบปยดหรอออาการไมเ บายนยอยเาเมาก ไมเร กยนกกเยลหรออซมเศรา

มปญหาในการเดนอยเาเมาก อาบน า หหรออใเเออาดยยนนเอเไมเได ไมเมปญหาในการท ากจกรรมทท าเปนประจ า มอาการเจบปยดหรอออาการไมเ บายนยอยเาเมาก ร กยนกกเยลหรออซมเศราอยเาเมากท ด


v. TTO feedback

After completed TTO valuation, the respondents were asked

how they agreed with 3 sentences: 1) It was easy to understand the

questions I was asked; 2) I found it easy to tell the difference

between the lives I was asked to think about; and 3) I found it

difficult to decide on the exact points where Life A and Life B were

about the same. The options were 5-point Likert scale where 1 =

agree and 5 = disagree (Appendix G).

vi. Preference elicitation by discrete choice experiment

There was 28 blocks of health state for DCE method

(Appendix H). Each block contained 7 pairs of health states; life A

and life B. Both life A and B consisted of 5 health state dimensions

with different level of severity. EQ-VT software randomly selected 1

block for each participant. The respondent was asked to imagine

both life A and B described on screen; and make a forced choice

from two alternative health states (Figure 3.3).

Figure 3.3 DCE screen

vii. DCE feedback

After completed DCE valuation, the respondents were asked

how they agreed with 3 sentences: 1) It was easy to understand the

มปญหาในการเดนอยเาเมาก

อาบน า หหรออใเเออาดยยนนเอเไมเได

ไมเมปญหาในการท ากจกรรมทท าเปนประจ า

มอาการเจบปยดหรอออาการไมเ บายนยอยเาเมาก

ร กยนกกเยลหรออซมเศราอยเาเมากท ด

มปญหาในการเดนเลกนอย

มปญหาในการอาบน า หหรออใเเออาดยยนนเอเเลกนอย

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ไมเร กยนกกเยลหรออซมเศรา

อะไรดกยเากน หชยนแบบ หA หรออชยนแบบ หB

A B


questions I was asked; 2) I found it easy to tell the difference

between the health states I was asked to think about; and 3) I found it

difficult to decide on my answers to the questions. The options were

5-point Likert scale where 1 = agree and 5 = disagree (Appendix I).

viii. Country-specific questionnaire

This paper-based questionnaire was created by Thai research

team. It contained 5 sentences regarding religious belief as follows.

1) Everything that happens in my life is a consequence of my actions

from my previous life.

2) When I am sick, there is someone looks after me.

3) Regardless of any serious sickness, I try to live as long as possible

to have something done.

4) According to my belief, to escape the problems by terminating

my own life is seriously wrong.

5) I hold the religious doctrine when I face the problems in my life.

The responses of country-specific questionnaire consisted of 5-

point Likert scales ranging from completely agree to completely

disagree (Appendix J).

ix. Qualitative questionnaire

This paper-based questionnaire was created by Thai research

team. It contained 3 questions (Appendix K); and was applied to

only some respondents who answered TTO questions with strange

reasons

5) At the end of interview, a screen expressed gratitude to the respondent

for the participation in the study. Respondents can give feedback by entering text in

the textbox of EQ-VT software.

6) The records were uploaded to the central data collection system of the

EuroQol group by the interviewers every day.

The respondents may control the device themselves. Nevertheless, Thai

people are not familiar with using computer device, some people may still unable to


use it after demonstration. In this case, the interviewer helped or controlled the device

instead.

Distinguish roles and download privileges for principal investigator (PI),

Data PI, and technical staffs were also specified in the protocol. Prior to data collection

begin, PI and co-investigators joined the 2-day EQ-VT training workshop offered by the

EuroQol group. Then, the interviewer was trained to fully acquaint with the operation

(installation of the EQ-VT offline, registration of respondents, data collection, and

uploading of data) and the central idea behind computer assisted application.

3.1.6 Study procedure

The procedures of the study were as follows.

1. Randomly selection of the area by the NSO

2. Coordination with study area’s coordinators.

3. Localized software development by EuroQol group and Thai team

4. Acquisition of the touch screen laptop

5. Translated interviewer instruction into Thai by EuroQol group and Thai

team

6. Recruiting and training 6 interviewers

7. Pilot testing (100 respondents in 8 primary care unit in Nonthaburi)

8. Data collection

9. Analysis and report

3.1.7 Data analysis for TTO valuation

3.1.7.1 Utility calculation

In order to calculate utility from TTO method, 2 different

methods were employed: method for better than dead (BTD) states and worse than

dead (WTD) states. Regarding BTD state, the utility (Ui) was calculated from x

divided by 10 (Ui = x/10) (48, 49), where x is number of year in full health of Life A

at the point of indifference. Regarding WTD state, the utility was calculated using lead

time approach which 10 years of full health were added to both alternative. The utility

was calculated from Ui = (x-10) / (10+t-10), when x is number of year in full health of

Life A at the point of indifference (51).


Figure 3.4 shows the example of utility calculation. The upper

picture shows the BTD state which the indifference point is 4 years for life A. Thus

the utility was 4/10 = 0.4. The lower picture shows the WTD state which the

indifference point is 8 years for life A. Thus the utility was (8-10)/(10+10-10) = -0.2.

Life A 4

Life B

Life A 8

Life B 10

Figure 3.4 Example of utility calculation for TTO valuation (51)

Dark cell = full health, diagonal cell = health state i, white cell = death

3.1.7.2 Data management for TTO data

Prior to input data into the regression model, the 7 criteria were

applied to detect low quality of the data as follows.

1) logical inconsistency

2) positive slope

3) all 10 health states got the same value

4) too many health states (> 8 health states) were valued as

WTD

5) too many health states (>8 health states) were valued as zero

6) very mild states (severity level = 6 or 7) were given very

low value (value < 0)

7) magnitude of inconsistency


Ui = 4/10 = 0.4

10 yr

Ui = (8-10)/(10+10-10) = -0.2 20 yr


Better than dead health state

Worse than dead health state


In our study, severity level of health state was calculated as the

summation of 5 numbers representing each dimension. For example, the severity level

of health state ‘21232’= 2+1+2+3+2 = 10. Thus the lowest possible severity level was

5 (11111) and the highest possible severity level was 25 (55555).

The explanation of 7 criteria was described as follows. The

logical inconsistency exists when the TTO score of health state 55555 (severity level = 25)

is lower than that of the mildest health state within TTO block, as shown in Figure 3.5.

The positive slope means that the line (10 dots/respondent)

plotted between TTO score (y-axis) and severity level of health state (x-axis) has

positive slope. This means that the respondent gave higher TTO score when the

severity level of health states increased.

When the respondent gave the same values for all 10 health

states, this kind of responses was unreasonable as there was a big difference between

the mildest health state and the worst health state (severity level = 25). Then, such

records should be excluded from the analysis.

The data which many health states (> 8 health states) were

valued as zero or below (WTD) was excluded also. The reason was that each block (10

health states) contained 1-3 milder states (including only a level 2 on 1-2 dimensions);

1-6 moderate states; and 3-7 severe states (including at least one of level 5 and one of

level 4 or 5) (93). Thus it was possible that the respondents values all severe states as

zero or WTD. However, the records which 8 or more health states were valued zero or

less than zero were excluded.


1. Logical inconsistency

2. Positive slope

3. All 10 states got the same value

4. Too many states were valued as WTD

5. Too many states were valued as zero

6. Very mild states got very low value

Figure 3.5 Six criteria used to detect low quality of the data for each respondent

After excluding respondents, whose at least 1 criteria were

met. The magnitude of inconsistency was calculated for each respondent. The

inconsistent response was defined as higher TTO score was assigned to a worse health

state (higher severity level). Table 3.1 demonstrates the calculation for the magnitude

of inconsistency of 1 respondent. The values in the table were calculated as ‘the TTO

score of milder state’ minus ‘the TTO score of more severe state’. The positive

number indicated consistent response while the negative number indicates the

inconsistency. As shown in Table 3.1, there were 3 inconsistency responses, i.e. 12513

Severity level

Severity level Severity level




vs 12344, 53221 vs 12344, and 14554 vs 44345, resulting in the magnitude of

inconsistency of 0.1+0.1+0.1 = 0.3.

Table 3.1 Magnitude of inconsistency responses of 1 respondent

Profile 11121 21112 12513 53221 12344 44125 54342 14554 44345 55555

Severity 6 7 12 13 14 16 18 19 20 25

Observed TTO score 1 1 0.9 0.9 1 0.8 0.8 0.7 0.8 0.4

11121 6 1

21112 7 1 0

12513 12 0.9 0.1 0.1

53221 13 0.9 0.1 0.1 0

12344 14 1 0 0 -0.1 -0.1 44125 16 0.8 0.2 0.2 0.1 0.1 0.2

54342 18 0.8 0.2 0.2 0.1 0.1 0.2 0

14554 19 0.7 0.3 0.3 0.2 0.2 0.3 0.1 0.1

44345 20 0.8 0.2 0.2 0.1 0.1 0.2 0 0 -0.1 55555 25 0.4 0.6 0.6 0.5 0.5 0.6 0.4 0.4 0.3 0.4

Since the appropriate cut-off point of magnitude of

inconsistence used to determine low quality data was unknown, this study divided

samples into subgroup according to the magnitude of inconsistent responses. Then

these subgroups were thoroughly compared using regression diagnostics. Finally, only

1 subgroup was chosen to estimate the Thai value sets.

3.1.7.3 Data diagnostic tests

Data diagnostic tests were used to check for potential problems

and evaluating the plausibility of key assumptions of multilevel analysis (81): 1)

linearity between dependent and independent variables; 2) homoscedasticity; and 3)

normal distributions of the residuals.

The linearity between dependent and independent variables

was demonstrated by the scatter plot between TTO value (Y-axis) and level of severity

(X- axis) of all respondents. However, the relation between y and x will not be seen

clearly when the sample size was large. The median trace was a crude way to show the

tendency in the relationship between y and x clearer (79). The median trace is a tool to

make a scatterplot smoother. To construct a median trace, the values on x-axis were


divided into strips and the median of y for each strip is calculated. Then the medians

are connected with straight lines.

Heteroscedasticity was inspected using the likelihood ratio test

(LR test). The normality of residuals was tested using Shapiro-Wilk test, Kernel

density estimate, and probability-probability (P-P) plot.

The specification test was performed by the Hausman test,

which was used to determine whether fixed effect or random effect models should be

used. In addition, a spaghetti plot, which is a method of viewing pattern of response of

each individual data, was also considered. It was plotted between severity level

(x-axis) and predicted utility (y-axis). Since our data was large, only 10 respondents

were selected to demonstrate the pattern of relationship.

3.1.7.4 Regression modeling

Multilevel regression models (i.e. random coefficient model)

were undertaken to estimate preference scores using STATA 12 as the dataset was

longitudinal or panel data (81). The dependent variable was disutility (i.e. 1 – TTO

score). The independent variables were 5 dimensions of EQ-5D which each dimension

contained 5 options. So 20 dummy variables were produced (Table 3.2), and they were

consisted in the main effect model. Our data can be a representative of Thai population

as weighted values were attached to each individual in the regression model.

Table 3.2 Variables in the TTO model

Variable Definition

MO2 1 if mobility is at level 2, 0 otherwise MO3 1 if mobility is at level 3, 0 otherwise MO4 1 if mobility is at level 4, 0 otherwise MO5 1 if mobility is at level 5, 0 otherwise SC2 1 if self-care is at level 2, 0 otherwise SC3 1 if self-care is at level 3, 0 otherwise SC4 1 if self-care is at level 4, 0 otherwise SC5 1 if self-care is at level 5, 0 otherwise UA2 1 if usual activity is at level 2, 0 otherwise UA3 1 if usual activity is at level 3, 0 otherwise UA4 1 if usual activity is at level 4, 0 otherwise UA5 1 if usual activity is at level 5, 0 otherwise


Table 3.2 Variables in the TTO model (cont.)

Variable Definition

PD2 1 if pain/discomfort is at level 2, 0 otherwise PD3 1 if pain/discomfort is at level 3, 0 otherwise PD4 1 if pain/discomfort is at level 4, 0 otherwise PD5 1 if pain/discomfort is at level 5, 0 otherwise AD2 1 if pain/discomfort is at level 2, 0 otherwise AD3 1 if pain/discomfort is at level 3, 0 otherwise AD4 1 if pain/discomfort is at level 4, 0 otherwise AD5 1 if pain/discomfort is at level 5, 0 otherwise

The various functional forms were employed to the data sets as

shown in Table 3.3. The interaction terms tested were N45, N5, and D123^2. N5 and

N45 were similar to the N3 term purposed by Dolan (94). The term N5, which was a

dichotomous variable, was defined as whether extreme problems (level 5) in any

domain exist. N5 = 1 if there were at least 1 dimension with level 5. N5 = 0 if no level

5 in the profile. The term N45 was defined as whether severe or extreme problems

(level 4 or 5) in any domain exist. N45 = 1 if there were at least 1 dimension with level

4 or 5. N45 = 0 if no level 4 or 5 in the profile. The term D123^2 was defined as the

number of dimensions at level 4 or 5 minus one and then squared. For instance, the

health state 34435 has 3 dimensions with level 4 or 5. Thus D123^2 is calculated as

(3-1)^2 = 4. Also the interactions between levels and different dimensions were tested,

i.e. MO3*UA5.

Table 3.3 Functional forms

Model Functional form

Model 1 Constant + main effect

Model 2 Main effect

Model 3 Constant + main effect + N45

Model 4 Main effect + N45

Model 5 Constant + main effect + N5

Model 6 Main effect + N5

Model 7 Constant + main effect + D123^2

Model 8 Main effect + D123^2


3.1.7.5 Evaluating the model performance

In order to select the best model for predicting the EQ-5D-5L

score for Thai people, four criteria were considered, i.e. consistency, predictive

performance, responsiveness and parsimony (85). Consistency means that more severe

problems associate with utility decrement (75, 95). Regarding consistency in the

regression model, the coefficient value of health dimension should associates with the

severity level of health dimension. Thus the inconsistence in the regression model

existed when the coefficients is lower when the severity level of health dimension

increases.

The predictive performance was evaluated using multiple

measures. Cross validation was employed by dividing the data set into 2 parts. Two-

third of data was used to generate a regression model, and one-third of data was kept as

testing data. The actual mean score from testing data was compared to the predicted

score by health profile. The number of health state was counted when the absolute

difference between the predicted score and mean actual score greater than 0.1 and 0.15.

The Akaike’s Information Criterion (AIC) was used for

comparing the fit of several models for the same data. The model with the lowest AIC

is likely to be the best model. This means that the model is considered to be closer to

the truth. Concordance correlation coefficient (CCC), was used to determine whether

the observed data significantly deviate from the line of perfect concordance (87). This

coefficient is not only measuring how far each observation deviates from the line fit to

the data (precision), but also how far this line deviates from the 45° line through the

origin (accuracy). The CCC value ranges from -1 to 1.

Bland-Altman plot was a graphical method used to show

prediction bias and precision (88). It plotted the difference of predicted score and

actual mean score on y-axis against the average of those score for each subject on x-axis.

The 95% limit of agreement was calculated as 1.96 x standard deviation. It is expected

that the 95% of dot (the difference between predicted score and actual mean score)

was within limits of agreement.

The standardized response mean (SRM) was used as an effect

size indices or reflected the responsiveness of health status change. The SRM was

obtained by dividing the mean score change by the standard deviation of the change.


Lastly, if two or more multiple regression models performed similarly, the simplest

model (model parsimony) or the model with fewer explanation variables was

preferred.

3.1.7.6 Detecting logical inconsistency among 3,125 health

states

To be confident that the predicted scores for 3,125 health states

are logical consistent according to Dolan and Kind method (77), this study employed a

simple Excel program to investigate. All 3,125 health states with its preference score

were arranged into 2 columns and sorted by number in sheet1 (Figure 3.6). In sheet2,

all 3,125 health states were arranged into 5 columns (i.e.column 1-5) and 625 rows.

These 5 columns represented 5 levels of mobility dimension, i.e. column 1 consisted

health profiles which the mobility was level 1, column 2 consisted health profiles

which the mobility was level 2, and so on. Each column, the health profiles was sorted

by number. The command vlookup was employed to link the matched preference

score from sheet1 to sheet2 into the next 5 column (i.e.column 6-10). Then the next 25

columns (i.e.column 11-35) were the results of subtracting between preference score

of less severe profile and more severe profile for only eligible health state pairs

according to Dolan and Kind method (77). The negative figures indicated inconsistent

preference scores which were counted using the command countif.


sheet1

Sheet2

Sheet2 (cont.)

Figure 3.6 Example of the method used to detect logical inconsistency

among 3,125 health states

Vlookup command was used to link preference score of matched profile from sheet1 to sheet2.

Cell M2 = G2-H2 Cell N2 = H2-I2 Cell O2 = I2-J2 …

Cell AI2 = G2-G3 Cell AJ2 = G3-G4 Cell AK2 = G4-G5 …


3.1.8 Data analysis for DCE valuation

Differently from TTO valuation, the values obtained from DCE valuation

were not directly observed and also have to be calculated from the binary responses.

The health state chosen by the respondents was assumed that it gave them higher

utility, so the conditional logit model can be employed to estimate health value (90,

91). The scale obtained from the conditional logit model is not the same as utility scale

where 0 refers to dead and 1 refers to perfect health. Thus the coefficient generated

from the conditional logit model cannot be used directly to calculate QALY. The

transformation or rescale is needed.

The dependent variable (Y) referred to the choice of respondents which

were rescaled to indicate that 0 stands for ‘not chosen health state’ and that 1 for ‘chosen

health state’. The independent variables consisted of 20 dummy variables. The value of

health state A of participant i (ViA) was explained by the following additive model (91).

Where XiAj are 20 dummy variables for participant i and health state A

(with 5 dimensions, each with 5 levels of severity). βj are the coefficients for each

independent variable j. Then the predictive model was consisted of ViA plus and error

term which was assumed to be random and show type 1 extreme value distribution.

Thus a conditional logistic model (clogit) can be applied (90, 91). The conditional

logistic model can be used to analyze the binary outcome data with one or more

predictors, where observations are not independent but are matched or grouped in

some way.

Consequently, the coefficients of the model were estimated. However the

values generated were on an arbitrary scale, they were needed to be rescaled. The

worst health state (health state 55555) predicted on the lead-time TTO model was

taken to anchor health state 55555 of DCE valuation in order to rescale the arbitrary

scale of the conditional logistic model. Thus, both TTO and DCE model of this study

produced the same index value for the worst health state. Rescale was undertaken by

dividing all coefficients obtained from a conditional logistic model by a scalar, which

was calculated as follows (91).

(worst health statedce – 1) / (worst health statetto – 1)


After rescale calculation, the utility decrements for each coefficient of

DCE model were obtained.

With regard to data management of DCE data, the previous study (90)

proposed 4 criteria to consider low quality data as follows.

1) All responses (each respondent valued 7 pairs health state) were on the

same side e.g. AAAAAAA, BBBBBBB.

2) The pattern of responses had particular order e.g. ABABABA,

BABABAB.

3) The respondents spent too little time choosing preferred health state

(defined as < 10 second/pair)

4) More severe health state was preferred. This criteria was defined if the

preferred health state was more severe than rejected health (i.e level of severity of the

preferred health state was at least 8 level higher than rejected health state) and at least

4 dimensions of the preferred health state were more severe than the rejected health

state (Table 3.4)

Table 3.4 Example of more severe health state was preferred

Patient ID

Health state

Level of severity Explanation

1 A 35554 22 If the respondent preferred health state A, this record was not removed because it met only 1 criterion. Health state A was 8 level more severe than health state B, however, only 3 dimensions of health state A were more severe than health state B.

B 55211 14

1 A 12151 10 If the respondent preferred health state B, this record was removed because it met both criteria. Health state B was 10 levels more severe than health state A; and 4 dimensions of health state B were more severe than health state A.

B 35543 20

However, the model from both cleaned data set and original data set were

generated and compared.


Session 3.2: Measurement properties of the Thai version of the 5L

compared to the 3L

3.2.1 Study design

This study was a cross-sectional survey.

3.2.2 Study location

The study was conducted at Ramathibodi Hospital, Bangkok between 7

January and 31 March 2013.

3.2.3 Study population

Participants in this study were those recruited in the study entitled

“Economic evaluation of self-monitoring of blood glucose (SMBG) intervention”. A

convenience sample of patients with type 1 and type 2 diabetes mellitus - who

received treatment at the outpatient department at Ramathibodi Hospital, Thailand,

was invited to participate. According to Terwee et al (52), the sample size was at least

50 respondents for subgroup.

The reasons for selection diabetic patient to test measurement properties of

the instrument were that the diabetic mellitus is a common chronic disease that

substantial affect quality of life of the patients. Additionally, diabetes was ranked as

third and eighth in terms of Disability-Adjusted Life Year (DALY) loss in Thai

women and men, respectively (96). The study by Holmes et al (97) demonstrated that

the quality of life of the people with diabetes was lower than general people in the

same age group. For these patients, their quality of life decreased with disease

progression and complications.

3.2.4 Selection criteria

For type 1 diabetic patient, eligibility criteria were 1) aged > 12 years old;

2) use insulin; 3) diagnosed with type 1 DM; and 4) consent to be interviewed.

For type 2 diabetic patient, eligibility crieteria were 1) aged > 18 years old;

2) use insulin; 3) diagnosed with type 2 DM; and 4) consent to be interviewed.

Pregnant women and disabled person were also excluded from the study.


3.2.5 Data collection method

The questionnaires used in the interview consisted of 4 parts as follows:

1) Demographic information (Appendix L)

2) One page of Thai version of the 3L and 5L; Visual Analog Scale (EQ

VAS) (Appendix L)

3) Short-form 36 health survey version 2 (SF-36v2) in Thai (Appendix M)

4) Preference question (Appendix L)

The single page of the 3L and the 5L response scale contained the 5L

version on the left column and the 3L version on the right column, as shown in

Appendix L. Similar to previous studies (21, 24, 26), respondents were asked to

complete the 5L first, followed by the 3L in order to avoid the tendency to not choose

levels 2 and 4 - the “in-between” options - when the 3L was completed first.

Preference questions comprised 2 items, 1) Which response scale did you find easier

to use (3L or 5L or indifferent? and 2) Which response scale did you think better

expresses your health? (21) The interviewer explained the instruction and let the

respondents completed the questionnaire by themselves. After that the interviewers

gave the second set of questionnaires, which consisted of the Thai version of one page

of Thai version of the 3L and 5L and SF-36v2 in a pre-paid envelop to the

respondents. Each respondent was asked to complete the second set of questionnaire at

2 weeks later, and send it back to the researcher.

This study was approved by the Mahidol University Institutional Review

Board (MU-IRB), Thailand and the Institute for the Development of Human Research

Protections (IHRP), Ministry of Public Health, Thailand (Appendix A). All

participants provided written informed consent and all instruments were self-

administered. After completing the questionnaire, the respondents received 3.25 USD

for compensation (1 USD = 30.73 Baht).

3.2.6 Data analysis

The utility index of the 5L was obtained from an interim mapping

generated by the EuroQol group (34) as the valuation study of the 5L in Thailand has


not yet been completed. The 3L utility index was calculated using the Thai value sets

developed by Tongsiri et al (12).

3.2.6.1 Distribution pattern and ceiling effect

The distribution of the 3L and 5L responses was demonstrated

in terms of percentage of each level reported. The redistribution patterns of the

responses from the 3L to 5L for each dimension were also reported in terms of

percentage. Similar to previous studies (21, 27), the response inconsistency and size

were determined and are shown in Table 3.5 To determine the inconsistency, the

response of the 3L was converted into the 5L (the 3L5L) as follows: 1 = 1, 2 = 3, and 3

= 5. Then, the size of inconsistency was calculated as |3L5L-5L|-1. A size of

inconsistency of ≤ 0 indicated consistency, and thus only 7 pairs are considered as

consistent responses.

Table 3.5 Size of (in) consistent response

3L 5L

level 1 level 2 level 3 level 4 level 5

level 1 -1 0 1 2 3

level 2 1 0 -1 0 1

level 3 3 2 1 0 -1

Adapted from Janssen et al (21). The size of inconsistency of < 0 indicated consistency. The number in dark cells represents the size of inconsistency.

For the ceiling effect, the proportion of respondents reported

‘no problems’ for all five dimensions - the proportion of respondents scoring ‘11111’

(22) - was compared for the 3L and the 5L. The percentage reduction from the 3L to

the 5L was calculated as follows: (Ceiling 3L – Ceiling 5L)/ Ceiling 3L. We

hypothesized that the ceiling effect should be lower in the 5L compared with the 3L.

Feasibility was assessed by calculating the number of missing values for the 3L and 5L.

3.2.6.2 Convergent validity

The convergent validity of the 5L and 3L were evaluated by

comparing them with the SF-36 as it is a widely-used generic health survey in clinical


research and has demonstrated validity among the Thai population (61, 67, 68). The

SF-36 contains 8 dimensions, i.e. physical functioning; role limitation due to physical

problems; bodily pain; general health perceptions; social functioning; vitality; role

limitations due to emotional problems; and general mental health (98). Since a

weighted Likert scale is used as the scoring system, the items for each dimension are

summed to provide a score which is then linearly transformed into a value from 0 – 100

(100 indicating the best health level).

Convergent validity was tested by assessing the relationship

between each dimension of the 5L and SF-36v2 using Spearman’s rank-order

correlation (Spearman’s rho). We hypothesized that each dimension in the 5L would

be more highly correlated to related subscales than to other subscales in the SF-36

compared to the 3L. The EQ-5D’s responses were recoded to signify that higher scores

presented better health statuses. The strength of correlation was determined as follows:

absent (r < 0.20), weak association (0.2≤ r< 0.35), moderate (0.35 ≤ r<0.50), and

strong (r≥0.50) (55).

3.2.6.3 Discriminative power

Discriminative power (or informativity) was determined by the

Shannon index ( ) and Shannon’s Evenness index ( ). These indices were initially

developed from the information theory (56) and are typically used to measure the

diversity and richness of information in ecosystems and the communications industry.

At present, and are often used to reflect the discriminatory power of health state

classification (21, 22, 24, 27, 56). High and values reflect discriminative power

and informativity of the instrument. The Shannon index ( ) is defined as (56):

Where;

= absolute amount of informativity captured


Pi = ni/N (the proportion of observations in the ith category (i = 1,...,C)

ni = the observed number of scores (responses) in category i

N = the total sample size


The Shannon index ( ) reflects the absolute information

content. The higher the , the more information is captured by the measure. The

also depends on the number of categories; if the number of levels (C) that are

actually needed increases, the index will also increase. However, if the newly added

levels are not used, the value will not increase. The index will reach its maximum

( max) which is equal to Log 2C (1.58 for 3L and 2.32 for 5L) when the optimal

amount of information is captured.

Shannon’s Evenness index ( ), on the other hand, expresses

the relative informativity of a system or the evenness of a distribution regardless of the

number of categories. is defined as:

In case of an even distribution, when all levels are filled with the

same frequency, is equal to 1. When comparing the 5L to the 3L, we expect the of

the 5L to be higher to reflect more discriminatory performance. On the other hand, the

of the 5L might slightly decrease as the extra level might not be used equally.

3.2.6.4 Test-retest reliability

The test-retest reliability of both EQ-5D index scores was

evaluated using the intraclass correlation coefficient (ICC) and the reliability of each

dimension was assessed with Cohen’s weighted kappa coefficient. According to

Fleiss’s standards for the strength of agreement for kappa values (53), Cohen’s

weighted kappa (k) was determined as follows: poor reproducibility (k < 0.4); good

reproducibility (0.4 < k < 0.75 ; excellent reproducibility (k > 0.75). Regarding intra-

rater reliability among each dimension at different times, the data set lacked variance

since most respondents responded with level 1 for self-care. The weighted kappa

coefficient could not be calculated, thus percentage agreement values was

demonstrated also (99, 100). It was calculated as: (a+d)/N, where the values of a and

d were obtained from a 2x2 table.


3.2.6.5 Data variability

Coefficient of variation (CV) was used to compare data

variability between the 3L and the 5L. It is obtained by the ratio of the standard

deviation to the mean; and multiplied with 100 to show in a percentage. Higher value

of CV indicates greater variability of data. The advantage is that CV allows

comparison between measures as the unit was cancelled, while standard deviation is

often difficult to interpret or compared with other measures as its value based on the

sample data. CV of this study was calculated from all respondents except for the

respondents who reported perfect health.

All data in this session were analyzed using SPSS 19 and Microsoft Excel

2013. Statistical significance was set a priori as p < 0.05.


preference score derived from the 3L and the 5L

In session 3.2, each participant answered both the 3L and the 5L. The 3L

utility was calculated using Thai value sets, which was estimated by classic TTO

technique (11, 12). The 5L utility was obtained from the value sets generated by

mapping method by EuroQol group (34); and also the Thai value sets obtained from

session 1. Utilities obtained from these 3 value sets were used to calculate QALY

(QALY = utility x life expectancy). Then, the QALY was inputting into the economic

evaluation model entitled “Economic evaluation of self-monitoring of blood glucose

(SMBG) intervention”. The incremental cost-effectiveness ratio (ICER) was used to

demonstrate the results of using 3 different value set. Given all parameters were

constant except for utility, ICER could be affected from QALY, which obtained from

different methods. The ICER was calculated as follows:

ICER = (C1-C2)/(Q1-Q2)

Where;

C1 = cost of reference intervention

C2 = cost of compared intervention

Q1 = QALY of reference intervention


Q2 = QALY of compared intervention

In order to cope with uncertainty of the parameters in the model, Monte

Carlo simulation was undertaken (101). It was done by input random parameters into

the model 1,500 times repeatedly. The results was plot on the cost-effectiveness plane

which the horizontal axis represented the incremental QALY and the vertical axis

represented incremental cost between the reference and compared interventions.

Uncertainty of the model was reflected by examining the distribution of those 1,500

dots. In addition, the cost-effectiveness acceptability curve (CEAC) was used to

summarize the information on uncertainty in cost-effectiveness analysis (35). It was

drawn by plotting the proportion of the costs and QALY pairs that were cost-effective

for the maximum acceptable ceiling ratio. The CEAC shows the probability that the

intervention is worth for a given value of the maximum acceptable ceiling ratio.

Head-to-head comparison of preference score of comparable health states

between the 3L and the 5L was also conducted. To identify comparable health states,

the level of 3L was converted to level of 5L as follows : level 23L to level 35L, level 33L

to level 55L and level13L to level 15L . For example, state 12323 of 3L was comparable

to state 13535 of the 5L.

Juntana Pattanaphesaj Results / 62

CHAPTER IV

RESULTS


scores for the 5L Thai version

4.1.1 Respondent’s characteristics

Table 4.1 presents the characteristic of 1,207 respondents interviewed. The

mean age of the respondents was 44 years old and about half of them (52%) were

female. A majority of respondent has been married (68%). Most of the respondent

graduated from primary school (44%), and high school (38%), respectively. Their

occupations were agriculture/fishery (35%), unskilled labor (19%), and business

owner (16%), respectively. The average household income of the respondents was

22,640 Baht per month. Table 4.2 shows that the percentage of the respondent from

the rural was slightly higher than those from urban (56% vs 44%).

Table 4.1 Demographic characteristic of respondents (n=1,207)

Demographic characteristic Mean (SD) Age (years) 43.55 (15.03) Household income (Baht / month) 22,640.36 (26,765.11) Number of child 1.75 (1.57)

n (%) Gender

Male 584 (48.38) Female 623 (51.62)

Marital status Married 817 (67.69) Single 230 (19.06) Widowed 88 (7.29) Divorced/Separated 72 (5.97)


Table 4.1 Demographic characteristic of respondents (n=1,207) (cont.)

Demographic characteristic n (%) Education

Primary school 526 (43.65) High school 458 (38.01) Bachelor’s degree 126 (10.46) Diploma 75 (6.22) Unlettered 16 (1.33) Master’s degree or higher 6 (0.50)

Occupation Agriculture/fishery 426 (35.29) Unskilled labor 227 (18.81) Business owner 193 (15.99) Housewife 128 (10.60) Student 65 (5.39) Government/state enterprise officer 45 (3.73) Employee 35 (2.90) Looking for a job 17 (1.41) Retired 16 (1.33) Unable to work due to sickness 7 (0.58) Other 48 (3.98)

Table 4.2 The number of respondents by age, gender, and residential area

Region / Province Age (SD) Gender (n (%)) Residential area (n (%))

Male Female Urban Rural

Bangkok 43.97 (15.49) 81 (48.21%) 87 (51.79%) 168 (100.00%) -

Central region Suphan Buri 43.90 (15.09) 109 (49.32%) 112 (50.68%) 70 (31.67%) 151 (68.33%)

Sing Buri 44.05 (15.57) 29 (47.54%) 32 (52.46%) 20 (32.79%) 41 (67.21%)

Sukhothai 43.38 (13.88) 13 (54.17%) 11 (45.83%) 9 (37.50%) 15 (62.50%)

Trat 42.80 (14.79) 28 (47.46%) 31 (52.54%) 20 (33.90%) 39 (66.10%)

North region Chiang Mai 42.80 (15.40) 60 (48.78%) 63 (51.22%) 80 (65.04%) 43 (34.96%)

Chiang Rai 43.25 (13.05) 32 (46.38%) 37 (53.62%) 29 (42.03%) 40 (57.97%)

Northeast region Nong Bua Lam Phu 43.48 (15.77) 23 (46.00%) 27 (54.00%) 20 (40.00%) 30 (60.00%)

Roi Et 43.43 (15.90) 72 (50.35%) 71 (49.65%) 50 (34.97%) 93 (65.03%)

Surin 43.87 (14.88) 66 (47.83%) 72 (52.17%) 20 (14.49%) 118 (85.51%)

South region Nakhon Si Thammarat 42.99 (14.48) 56 (46.28%) 65 (53.72%) 30 (24.79%) 91 (75.21%)

Krabi 44.40 (14.93) 15 (50.00%) 15 (50.00%) 10 (33.33%) 20 (66.67%)

Total 43.55 (15.03) 584 (48.38%) 623 (51.62%) 526 (43.58%) 681 (56.42%)


Regarding health status of the respondents measured by the EQ-5D-5L, a

majority of them reported ‘no problem’ for each dimension; ranging from 47% for

pain/discomfort dimension to 96% for self-care dimension (Table 4.3). The mean VAS

score was 83. Out of a total of 1,207 respondents, 366 respondents (30%) reported

their own health as full health (11111). Only 1 respondent reported extreme problem

in dimension anxiety/depression. The mean time of computer-based interviewing was

37 minutes (SD = 12.65). This time was not include the process prior and after

computer-based interviewing, i.e. inform consent, interviewing with paper-based

country-specific question, and qualitative questions.

Table 4.3 Health status of respondents by level of severity

Dimensions No problems Slight problems

Moderate problems

Severe problems

Extreme

N % N % N % N % N % Mobility 873 72.33% 235 19.47% 84 6.96% 15 1.24% - Self-care 1,163 96.35% 32 2.65% 9 0.75% 3 0.25% - Usual activities 952 78.87% 188 15.58% 60 4.97% 7 0.58% - Pain/discomfort 571 47.31% 525 43.5% 98 8.12% 13 1.08% - Anxiety/depression 823 68.19% 313 25.93% 62 5.14% 8 0.66% 1 0.08

4.1.2 Valuation by TTO

The mean time used for 1 TTO task was 76.27 seconds (sd=56.08). It

should be noted that each respondent valued 10 TTO tasks. Table 4.4 presents the

mean observed value of 86 health state’s profiles, the highest mean TTO score was

0.94 for the following 4 health states: 11112, 11121, 12111, 21111. The lowest mean

TTO score was -0.31 for health state 55555. Eight out of 86 health states were valued

as worse than death, i.e. 35245, 55225, 44345, 55424, 44553, 52455, 43555, 55555.

With regard to TTO feedback questions, most of Thai respondents

understand the TTO questions, however it was not easy to differentiate between the

lives they were asked to imagine. They agreed that it was difficult to decide on the

exact points where Life A and Life B were about the same (Table 4.5).


Table 4.4 Observed mean values by health state’s profiles

Health state

Severity Mean SD Health state

Severity Mean SD Health state

Severity Mean SD

11112 6 0.94 0.01 23242 13 0.59 0.03 45133 16 0.33 0.03

11121 6 0.94 0.01 25222 13 0.68 0.02 51451 16 0.24 0.04

11211 6 0.93 0.01 32314 13 0.61 0.03 24443 17 0.19 0.04

12111 6 0.94 0.01 35311 13 0.59 0.02 34244 17 0.18 0.04

21111 6 0.94 0.01 42115 13 0.53 0.03 43514 17 0.25 0.04

11122 7 0.91 0.01 53221 13 0.54 0.03 45233 17 0.27 0.04

11212 7 0.88 0.01 12344 14 0.52 0.02 45413 17 0.20 0.04

11221 7 0.87 0.01 25331 14 0.59 0.02 53243 17 0.22 0.04

12112 7 0.88 0.01 31514 14 0.50 0.03 34155 18 0.06 0.04

12121 7 0.87 0.01 34232 14 0.56 0.02 34515 18 0.14 0.04

21112 7 0.88 0.01 51152 14 0.36 0.03 43542 18 0.13 0.04

11421 9 0.77 0.02 12543 15 0.50 0.03 45144 18 0.03 0.05

13122 9 0.79 0.02 21345 15 0.39 0.04 52335 18 0.24 0.04

14113 10 0.76 0.01 21444 15 0.39 0.03 53244 18 0.08 0.04

11414 11 0.67 0.02 22434 15 0.41 0.03 54153 18 0.06 0.04

13313 11 0.74 0.01 23514 15 0.42 0.03 54342 18 0.16 0.04

11235 12 0.61 0.03 24342 15 0.41 0.03 55233 18 0.19 0.04

12513 12 0.65 0.02 31524 15 0.41 0.03 14554 19 0.13 0.04

13224 12 0.68 0.02 52215 15 0.36 0.03 24445 19 0.02 0.05

21315 12 0.60 0.03 52431 15 0.40 0.04 24553 19 0.04 0.04

25122 12 0.63 0.03 53412 15 0.33 0.04 35245 19 -0.02 0.05

42321 12 0.61 0.03 54231 15 0.38 0.04 55225 19 -0.01 0.05

11425 13 0.61 0.02 31525 16 0.38 0.04 44345 20 -0.02 0.04

12244 13 0.51 0.03 32443 16 0.37 0.03 55424 20 -0.01 0.04

12334 13 0.57 0.03 33253 16 0.30 0.04 44553 21 -0.02 0.05

12514 13 0.48 0.03 35143 16 0.39 0.03 52455 21 -0.03 0.05

15151 13 0.50 0.03 35332 16 0.38 0.03 43555 22 -0.10 0.05

21334 13 0.57 0.02 43315 16 0.30 0.04 55555 25 -0.31 0.01

23152 13 0.58 0.03 44125 16 0.35 0.03


Table 4.5 TTO feedback

Questions Completely agree Agree Neutral Disagree Completely

disagree Question 1 146 (12.10%) 606 (50.21%) 385 (31.90%) 63 (5.22%) 7 (0.58%) Question 2 39 (3.23%) 274 (22.7%) 346 (28.67%) 473 (39.19%) 75 (6.21%) Question 3 108 (8.95%) 639 (52.94%) 355 (29.41%) 97 (8.04%) 8 (0.66%) Question 1 : It was easy to understand the questions I was asked.

Question 2 : I found it easy to tell the difference between the lives I was asked to think about.

Question 3 : I found it difficult to decide on the exact points where Life A and Life B were about the same.

4.1.3 Data diagnostic tests

The models were checked for potential problems and evaluating the

plausibility of key assumptions of multilevel analysis (81): 1) linearity between

dependent and independent variables; 2) homoscedasticity; and 3) normal distributions

of the residuals.

The linearity between dependent and independent variables was

demonstrated by the scatter plot between TTO value (Y-axis) and level of severity (X- axis)

of all respondents with median band. Figure 4.1 reveals that the relationship between

TTO value and level of severity is linear. Thus linear regression could be applied to

our data set.

-1-.

50

.51

5 10 15 20 25Severity

Value Median bands

Figure 4.1 Scatter plot between TTO value (Y) and level of severity (X) of all

respondents


A spaghetti plot is a method of viewing individual data to visualize the

relationship between x and fitted y. Since our data was large, 10 respondents were

selected to show the pattern of relationship. Figure 4.2 demonstrates that both the

intercept and slope of the data are random. Thus random effect model could be used to

analyze the data of this study.

-2-1

01

2

Fitt

ed v

alue

s

5 10 15 20 25severity

Spaghetti plot of value severity id int_id1

Figure 4.2 spaghetti plot between fitted value (Y) and level of severity (X) of 10

selected respondents

Each model was tested for the mispecification using the Hausman test. The

Hausman test was used to determine whether fixed effect or random effect models

should be used. Heteroscedasticity of all models was inspected using the likelihood

ratio test (LR test). According to the test, heteroscedasticity was found in all models.

So, robust standard errors were employed in the model estimation.

The normality of residuals was tested using Shapiro-Wilk test, Kernel

density estimate, and probability-probability (P-P) plot. The Shapiro-Wilk test

indicated that the distribution of the data was not normality. However, the inspection

by visual graph (Figure 4.3 and 4.4) showed that the distribution of the data was near

normal distribution.


0.5

11.

52

Den

sity

-1 -.5 0 .5 1 1.5Residuals

Kernel density estimateNormal density

kernel = epanechnikov, bandwidth = 0.0304

Kernel density estimate

Figure 4.3 Kernel density estimate of residuals

0.00

0.25

0.50

0.75

1.00

Nor

mal

F[(

resi

d_R

coef

-m)/

s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

Figure 4.4 Probability-probability (P-P) plot of residuals

4.1.4 Data selection to input in the regression model

According to the low quality data criteria (Table 4.6), twenty six out of

1,207 cases met the criteria. After excluding the 26 cases with low quality, magnitude


of inconsistency was calculate for each of the remaining cases (1,181 cases). Magnitude

of inconsistency per respondent ranged from 0 to 10, as shown in Figure 4.5.

Table 4.6 Number of respondents that met the criteria for low quality data for TTO

valuation

Criteria Number of

respondents*

1. logical inconsistency 2 (0.2%)

2. positive slope 1 (0.1%)

3. all 10 health states got the same value 0 (0.0%)

4. too many health states (> 8 health states) were valued as WTD 23 (1.9%)

5. too many health states (>8 health states) were valued as zero 1 (0.1%)

6. very mild states (severity level = 6 or 7) were given very low

value (value < 0)

5 (0.4%)

* The number of case can be double counted.

Figure 4.5 Magnitude of inconsistency among 1,181 respondents

Then, all 1,181 respondents were arbitrary divided into 3 groups (A, B, C)

according to the magnitude of inconsistency: group A consisted of 438 respondents

whose no inconsistency was found; group B consisted of 702 respondents whose

magnitude of inconsistency ranged between 0.01-2.99; and group C consisted of 41

respondents whose magnitude of inconsistency ranged between 3.0-10.0. By using

No. of respondent

Magnitude of inconsistency


three as a cut-off point for magnitude of inconsistency, only 41 respondents (3.4%) of

sample will be excluded if group C showed poor fit statistics. It should be noted that if

cut-off point between group B and C was two, 72 respondents (6%) of sample will be

excluded.

By using both data quality criteria and magnitude of inconsistency, 4

subgroups were classified, as shown in Table 4.7. Subgroup 1 consisted of the

respondents whose data quality was satisfied and no inconsistency was found.

Subgroup 2 consisted of the respondents whose data quality was satisfied and

magnitude of inconsistency was lower than 3.0. Subgroup 3 consisted of the

respondents whose data quality was satisfied and their magnitude of inconsistency was

lower than 10.0. Lastly, subgroup 4 was the total respondents. These 4 subgroups were

compared in terms of mean TTO score, parameter estimates and the fit statistics in

order to find out the best subgroup to estimate Thai value sets. Table 4.8 and Figure

4.6 presents the mean TTO scores of 4 these subgroups.

Table 4.7 Subgroup classification by low quality data criteria and magnitude of

inconsistency

Subgroup Inclusion of

low quality data Magnitude of inconsistent value n

1 No 0 (group A) 438 2 No 0 – 2.99 (group A +B) 1,140 3 No 0 – 10 (group A+B+C) 1,181 4 Yes 0 – 10 (group A +B+C) 1,207


Table 4.8 Mean TTO scores by subgroup

Profile Subgroup 1 Subgroup 2 Subgroup 3 Subgroup 4

Mean SD n Mean SD n Mean SD n Mean SD n

11112 0.941 0.070 87 0.937 0.084 219 0.936 0.085 225 0.932 0.096 229

11121 0.947 0.068 86 0.935 0.086 236 0.934 0.086 246 0.931 0.094 255

11211 0.933 0.086 106 0.926 0.091 239 0.926 0.092 249 0.923 0.100 254

12111 0.954 0.066 94 0.941 0.082 227 0.938 0.086 233 0.928 0.152 240

21111 0.945 0.065 65 0.938 0.081 219 0.940 0.080 228 0.933 0.140 229

11122 0.920 0.091 49 0.907 0.100 116 0.908 0.099 117 0.883 0.216 120

11212 0.895 0.099 29 0.876 0.116 113 0.879 0.115 117 0.879 0.115 117

11221 0.892 0.103 45 0.881 0.116 111 0.872 0.127 116 0.870 0.128 120

12112 0.888 0.102 29 0.873 0.110 113 0.876 0.109 117 0.876 0.109 117

12121 0.877 0.126 76 0.858 0.138 134 0.859 0.138 135 0.854 0.148 139

21112 0.882 0.118 49 0.877 0.122 125 0.877 0.121 131 0.873 0.140 134

11421 0.817 0.107 36 0.780 0.151 106 0.770 0.167 111 0.757 0.217 112

13122 0.825 0.099 30 0.795 0.134 105 0.784 0.179 114 0.783 0.179 115

14113 0.796 0.125 39 0.760 0.139 106 0.754 0.141 112 0.752 0.141 114

11414 0.757 0.108 37 0.692 0.171 111 0.670 0.223 115 0.590 0.415 121

13313 0.756 0.120 36 0.734 0.153 106 0.736 0.156 111 0.722 0.213 112

11235 0.713 0.165 45 0.646 0.218 111 0.625 0.259 116 0.590 0.319 120

12513 0.681 0.133 49 0.634 0.222 125 0.616 0.277 131 0.580 0.362 134

13224 0.721 0.134 49 0.695 0.202 116 0.694 0.202 117 0.670 0.271 120

21315 0.690 0.146 39 0.622 0.217 106 0.615 0.232 112 0.593 0.288 114

25122 0.688 0.131 36 0.626 0.268 106 0.617 0.296 111 0.606 0.317 112

42321 0.679 0.158 49 0.634 0.222 116 0.621 0.263 117 0.598 0.313 120

11425 0.687 0.149 30 0.647 0.151 105 0.607 0.232 114 0.600 0.243 115

12244 0.621 0.143 36 0.516 0.261 106 0.491 0.302 111 0.480 0.324 112

12334 0.615 0.208 48 0.553 0.311 113 0.553 0.311 113 0.548 0.312 115

12514 0.594 0.183 45 0.532 0.264 111 0.499 0.326 116 0.462 0.382 120

15151 0.578 0.227 39 0.504 0.294 106 0.473 0.350 112 0.457 0.369 114

21334 0.600 0.195 48 0.549 0.285 113 0.549 0.285 113 0.535 0.304 115

23152 0.669 0.113 29 0.592 0.262 113 0.585 0.277 117 0.585 0.277 117

23242 0.661 0.162 48 0.569 0.321 113 0.569 0.321 113 0.566 0.324 115

25222 0.650 0.182 37 0.687 0.164 111 0.681 0.182 115 0.604 0.386 121

32314 0.663 0.200 48 0.590 0.317 113 0.590 0.317 113 0.580 0.344 115

35311 0.590 0.159 49 0.600 0.207 116 0.599 0.207 117 0.584 0.234 120

42115 0.595 0.157 30 0.561 0.218 105 0.515 0.321 114 0.507 0.331 115

53221 0.552 0.216 49 0.530 0.307 125 0.493 0.350 131 0.460 0.409 134

12344 0.468 0.229 49 0.480 0.298 125 0.474 0.308 131 0.442 0.373 134

25331 0.555 0.314 37 0.601 0.232 111 0.595 0.247 115 0.530 0.387 121

31514 0.489 0.313 37 0.519 0.255 111 0.503 0.279 115 0.432 0.414 121

34232 0.514 0.215 49 0.573 0.214 116 0.571 0.215 117 0.547 0.277 120

51152 0.502 0.224 30 0.408 0.306 105 0.343 0.385 114 0.338 0.388 115

12543 0.540 0.288 76 0.501 0.338 134 0.500 0.336 135 0.472 0.372 139

21345 0.553 0.130 29 0.384 0.407 113 0.383 0.403 117 0.383 0.403 117

21444 0.403 0.337 37 0.402 0.333 111 0.392 0.341 115 0.325 0.444 121


Table 4.8 Mean TTO scores by subgroup (cont.)

Profile Subgroup 1 Subgroup 2 Subgroup 3 Subgroup 4

Mean SD n Mean SD n Mean SD n Mean SD n

22434 0.405 0.294 30 0.442 0.310 105 0.398 0.380 114 0.392 0.384 115

23514 0.419 0.329 76 0.433 0.321 134 0.433 0.320 135 0.405 0.358 139

24342 0.432 0.315 48 0.389 0.376 113 0.389 0.376 113 0.390 0.377 115

31524 0.440 0.261 39 0.418 0.315 106 0.399 0.351 112 0.382 0.373 114

52215 0.435 0.318 76 0.366 0.397 134 0.356 0.412 135 0.331 0.435 139

52431 0.473 0.268 39 0.417 0.338 106 0.416 0.336 112 0.397 0.364 114

53412 0.359 0.338 48 0.331 0.403 113 0.331 0.403 113 0.330 0.401 115

54231 0.451 0.297 45 0.424 0.359 111 0.381 0.420 116 0.354 0.441 120

31525 0.500 0.205 36 0.411 0.349 106 0.369 0.399 111 0.359 0.409 112

32443 0.290 0.377 76 0.392 0.335 134 0.393 0.334 135 0.364 0.371 139

33253 0.197 0.403 48 0.296 0.403 113 0.296 0.403 113 0.299 0.403 115

35143 0.332 0.356 37 0.384 0.345 111 0.385 0.348 115 0.319 0.447 121

35332 0.300 0.362 30 0.394 0.340 105 0.376 0.359 114 0.371 0.362 115

43315 0.333 0.258 39 0.306 0.368 106 0.304 0.386 112 0.287 0.405 114

44125 0.319 0.305 49 0.328 0.363 125 0.316 0.376 131 0.287 0.419 134

45133 0.242 0.406 76 0.354 0.366 134 0.353 0.365 135 0.324 0.402 139

51451 0.326 0.349 45 0.277 0.385 111 0.246 0.419 116 0.222 0.433 120

24443 0.208 0.331 39 0.178 0.439 106 0.190 0.432 112 0.175 0.444 114

34244 0.426 0.137 29 0.194 0.435 113 0.168 0.463 117 0.168 0.463 117

43514 0.483 0.174 29 0.266 0.437 113 0.250 0.449 117 0.250 0.449 117

45233 0.361 0.296 36 0.289 0.403 106 0.285 0.418 111 0.275 0.429 112

45413 0.208 0.378 30 0.224 0.423 105 0.188 0.454 114 0.184 0.454 115

53243 0.228 0.359 37 0.233 0.390 111 0.225 0.396 115 0.168 0.462 121

34155 -0.049 0.476 76 0.058 0.484 134 0.061 0.484 135 0.043 0.490 139

34515 0.179 0.415 45 0.171 0.431 111 0.157 0.442 116 0.130 0.461 120

43542 0.024 0.436 76 0.124 0.460 134 0.126 0.459 135 0.106 0.469 139

45144 0.061 0.440 45 0.058 0.472 111 0.045 0.481 116 0.022 0.492 120

52335 0.155 0.432 49 0.242 0.435 116 0.241 0.433 117 0.225 0.449 120

53244 -0.023 0.455 37 0.079 0.452 111 0.076 0.452 115 0.027 0.491 121

54153 0.071 0.384 39 0.054 0.462 106 0.058 0.463 112 0.046 0.471 114

54342 0.142 0.388 49 0.164 0.432 125 0.133 0.449 131 0.108 0.474 134

55233 0.258 0.357 36 0.200 0.432 106 0.201 0.431 111 0.194 0.436 112

14554 0.020 0.416 49 0.092 0.449 125 0.088 0.466 131 0.064 0.487 134

24445 -0.098 0.504 49 0.019 0.482 116 0.010 0.489 117 0.007 0.487 120

24553 -0.003 0.456 30 0.076 0.465 105 0.031 0.484 114 0.027 0.483 115

35245 -0.054 0.451 45 0.027 0.473 111 0.024 0.475 116 0.000 0.487 120

55225 -0.048 0.445 48 -0.032 0.515 113 -0.032 0.515 113 -0.035 0.512 115

44345 -0.111 0.436 49 -0.048 0.486 125 -0.050 0.483 131 -0.071 0.497 134

55424 0.307 0.158 29 -0.010 0.484 113 -0.013 0.479 117 -0.013 0.479 117

44553 0.253 0.160 29 -0.037 0.476 113 -0.031 0.485 117 -0.031 0.485 117

52455 0.122 0.400 36 -0.020 0.479 106 -0.036 0.494 111 -0.043 0.497 112

43555 -0.229 0.488 49 -0.088 0.515 116 -0.095 0.519 117 -0.108 0.526 120

55555 -0.271 0.454 438 -0.292 0.485 1140 -0.306 0.487 1181 -0.317 0.489 1207


Figure 4.6 Mean TTO scores by subgroup

Random effect model was employed to 4 subgroups to generate parameters

and fit statistics as shown in Table 4.9. Cross validation demonstrated that subgroup 2

performed the best as the absolute difference between actual mean scores and

predicted scores was the lowest. In terms of fit statistics (i.e. R square, RMSE, CCC,

and Cohen effect), compared to subgroup 2-4, subgroup 1 was the best, however it

produced too many number of states with absolute difference > 0.1. This may occur

because low number of sample size of subgroup 1 led to more variability between

individual although the inconsistency within individual was the lowest. The data from

subgroup 2 showed the best validation and acceptable fit statistics, so it was selected

to test the functional forms and estimate the Thai value sets.

Table 4.9 parameter estimates and the fit statistics by subgroup

Parameters Subgroup 1 Subgroup 2 Subgroup 3 Subgroup 4 (n=438) (n=1,140) (n=1,181) (n=1,207)

Cross validation - Number of inconsistent

coefficient in the regression model

0

0

0

0

- Number of states with absolute difference*> 0.1 (out of 86 health states)

30 11 14 22


Table 4.9 parameter estimates and the fit statistics by subgroup (cont.)

Parameters Subgroup 1 Subgroup 2 Subgroup 3 Subgroup 4 (n=438) (n=1,140) (n=1,181) (n=1,207)

- Number of states with

absolute difference > 0.15 (out of 86 health states)

9 4 5 9

Maximum score

0.9817 (11211)

0.9607 (12111)

0.9555 (12111)

0.9458 (12111)

Minimum score (55555)

-0.2838 -0.3015 -0.3166 -0.3296

Range from the best to the worst score

1.2838 1.3015 1.3166 1.3296

R square 0.57 0.52 0.50 0.47 RMSE 0.24 0.27 0.28 0.29 CCC 0.73 0.68 0.67 0.64 Cohen effect 1.36 1.07 1.01 0.90 * absolute difference = the difference between actual mean scores and predicted scores

4.1.5 Testing for the functional form

The weighted data set of subgroup 2 was tested with various functional

forms, however only 8 functional forms were reported, i.e. with/without constant,

with/without N45 term, with/without N5 term, and with/without D123^2 term. Table

4.10 demonstrates coefficients and fit statistics generated from 8 functional forms. No

inconsistent coefficients was found among 20 dummy variables, however, the

coefficient of interaction terms (i.e. N45) were inconsistent because of negative value. No

inconsistent preference scores found was among 3,125 health states for all 8 models.

According to Figure 4.7, the predicted score from model 5 and 6 showed

unacceptable deviation from actual mean score. In addition, Bland-Altman plot

(Figure 4.8) confirmed that model 5 and 6 showed the most bias since only 73% and

87% of dot were covered with 95% confident interval (Table 4.10). Thus model 5 and

6 were excluded.

According to Figure 4.7, the predicted score from model 7 and 8 shows

more deviation from actual mean score when the predicted score (Y-axis) is lower

than 0.2 compared with model 1 and 2. The Bland-Altman plots also demonstrates that

model 7 and 8 has more prediction bias as 6% of dot are out of 95% confident interval

range (Table 4.10). Thus model 7 and 8 were excluded.


In terms of AIC and responsiveness (measured by SRM), model 3 and 4 was

better than model 1 and 2. However, the coefficient of interaction term (N45) of model 3

and 4 was inconsistent because of negative value. Thus model 3 and 4 were excluded.

Bland-Altman plot showed that model 1 and 2 had lowest prediction bias as 96.5% of dot

were covered by 95% confident interval range. Model 1 and 2, which offered less

variables or more parsimony, were better.

Compared with model 1, model 2 was better in terms of higher

responsiveness and more parsimony. Thus the model 2 which was the model without

constant and interaction terms was selected to estimate the Thai value sets.

Table 4.10 Coefficients and fit statistics generated from data of subgroup 2 by functional form

Functional form

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Constant

+ main effect

Main effect Constant

+ main effect + N45a

Main effect

+ N45

Constant + main effect

+ N5b

Main effect

+ N5


+ D123^2c

Main effect + D123^2

Coef SE Coeff SE Coeff SE Coeff SE Coeff SE Coeff SE Coeff SE Coeff SE

MO2 0.052 0.009 0.056 0.009 0.072 0.010 0.075 0.009 0.052 0.010 0.056 0.009 0.058 0.010 0.062 0.009 MO3 0.112 0.010 0.114 0.010 0.137 0.010 0.139 0.010 0.112 0.011 0.114 0.011 0.122 0.010 0.125 0.010 MO4 0.229 0.010 0.231 0.010 0.256 0.011 0.259 0.010 0.228 0.010 0.231 0.010 0.227 0.010 0.230 0.010 MO5 0.306 0.011 0.307 0.011 0.324 0.011 0.325 0.011 0.306 0.011 0.307 0.011 0.300 0.011 0.301 0.011

SC2 0.028 0.008 0.033 0.007 0.045 0.008 0.049 0.007 0.028 0.008 0.033 0.007 0.032 0.008 0.036 0.007 SC3 0.107 0.009 0.108 0.009 0.121 0.009 0.122 0.009 0.107 0.009 0.108 0.009 0.109 0.009 0.111 0.009 SC4 0.223 0.012 0.225 0.012 0.242 0.012 0.243 0.012 0.223 0.012 0.225 0.012 0.216 0.012 0.217 0.012 SC5 0.252 0.009 0.254 0.009 0.269 0.009 0.271 0.009 0.252 0.009 0.253 0.009 0.237 0.011 0.237 0.011

UA2 0.038 0.008 0.043 0.008 0.056 0.009 0.061 0.008 0.038 0.008 0.043 0.008 0.041 0.009 0.045 0.008 UA3 0.070 0.011 0.075 0.011 0.092 0.011 0.097 0.011 0.070 0.011 0.075 0.011 0.073 0.011 0.077 0.011 UA4 0.161 0.009 0.165 0.009 0.191 0.011 0.194 0.010 0.161 0.009 0.165 0.009 0.155 0.010 0.158 0.009 UA5 0.205 0.009 0.207 0.009 0.224 0.010 0.226 0.009 0.205 0.009 0.207 0.009 0.192 0.010 0.194 0.010

PD2 0.034 0.008 0.040 0.007 0.049 0.008 0.053 0.007 0.034 0.008 0.040 0.007 0.038 0.008 0.042 0.007 PD3 0.068 0.010 0.068 0.010 0.086 0.011 0.086 0.011 0.068 0.010 0.068 0.010 0.072 0.010 0.073 0.010 PD4 0.234 0.010 0.233 0.010 0.247 0.010 0.246 0.010 0.234 0.010 0.234 0.010 0.226 0.010 0.225 0.010 PD5 0.262 0.011 0.266 0.011 0.283 0.012 0.287 0.011 0.262 0.011 0.266 0.011 0.250 0.011 0.253 0.011

AD2 0.025 0.008 0.032 0.007 0.035 0.008 0.041 0.007 0.025 0.008 0.032 0.007 0.028 0.008 0.034 0.007 AD3 0.091 0.011 0.097 0.010 0.109 0.011 0.113 0.011 0.091 0.011 0.096 0.011 0.098 0.011 0.103 0.011 AD4 0.198 0.009 0.202 0.009 0.227 0.010 0.232 0.009 0.198 0.009 0.202 0.009 0.195 0.010 0.198 0.009 AD5 0.245 0.010 0.249 0.010 0.265 0.011 0.268 0.011 0.245 0.010 0.249 0.010 0.236 0.011 0.239 0.011 Const 0.020 0.007 - - 0.017 0.007 - - 0.020 0.007 0.016 0.008 - - N45 - - - - -0.084 0.012 -0.085 0.012 - - - - - - N5 - - - - - - - - 0.000 0.009 0.001 0.009 - - - -

D123^2 - - - - - - - - - - - - 0.004 0.002 0.004 0.002

Juntana Pattanaphesaj R

esults / 76

Table 4.10 Coefficients and fit statistics generated from data of subgroup 2 by functional form (cont.)

Functional form

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Constant

+ main effect

Main effect Constant

+ main effect + N45a

Main effect

+ N45


+ N5b

Main effect

+ N5


+ D123^2c

Main effect + D123^2

Second best score (11112)

0.9553 0.9679 0.9480 0.9558 0.9554 0.9680 0.9522 0.9621

Minimum score (55555)

-0.2896 -0.2832 -0.2973 -0.2918 -0.2894 -0.2829 -0.2357 -0.2269

Range from the best to worst score

1.2896 1.2832 1.2973 1.2918 1.2894 1.2829 1.2357 1.2269

Inconsistent score among 3,125 health states

0 0 0 0 0 0 0 0

Number of negative value among 3,125 health state

135 126 151 139 135 126 94 86

AIC 4987 4987 4927 4927 4987 4989 4975 4974 CCC 0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68 SRM 1.68 1.75 1.82 1.86 1.78 1.85 1.82 1.85 % dots within limits of agreement from Bland-Altman plot

96.5 96.5 94.2 94.2 73.3 87.2 94.2 94.2

a The term N45 is defined as whether severe or extreme problems (level 4 or 5) exist in any domain. N45 = 1 if there are at least 1 dimension with level 4 or 5. N45 = 0 if no level 4 or 5 in the profile. b The term N5 is defined as whether extreme problems (level 5) exist in any domain. N5 = 1 if there are at least 1 dimension with level 5. N5 = 0 if no level 5 in the profile. c The term D123^2 is defined as the number of dimensions at level 4 or 5 minus one and then squared. For instance, the health state 34435 has 3 dimensions with level 4 or 5. Thus D123^2 is calculated as (3-1)^2 = 4.

Ph.D. (Pharm

acy Adm

inistration) / 77

Fac. of Grad. Studies, M

ahidol Univ.


Figure 4.7 Comparison between actual mean score and predicted score of the model

1-8 using data from subgroup 2

Pre

dict

ed s

core

Health state

Health state

Pre

dict

ed s

core

Health state

Pre

dict

ed s

core


Figure 4.8 Bland-Altman plots of model 1-8

X-axis = mean of actual and predicted scores; Y-axis = difference of actual and predicted scores; The two dash lines are upper and lower limit of 95% confidence interval. The percent of dot that was out of 95%CI line of model 1-8 was 3, 3, 5, 5, 23, 11, 5 and 5%, respectively.


4.1.6 The Thai algorithm and the preference scores

The Thai algorithm and the preference scores were estimated using the

weighted data from subgroup 2 and model 2 which had no constant and interaction

terms. Table 4.11 presents the coefficients of the Thai model. Thai preference scores

are calculated by 1 – disutility using the following algorithm.

Thai score for EQ-5D-5L = 1-(0.056*MO2)-(0.114*MO3)-(0.231*MO4)-

(0.307*MO5)-(0.033*SC2)-(0.108*SC3)-(0.225*SC4)-

(0.254*SC5)-(0.043*UA2)-(0.075*UA3)-(0.165*UA4)-

(0.207*UA5)-(0.040*PD2)-(0.068*PD3)-(0.233*PD4)-

(0.266*PD5)-(0.032*AD2)-(0.097*AD3)-(0.202*AD4)-

(0.249*AD5)

Where

MO2, SC2, UA2, PD2, AD2 = 1 if level 2 is responded, 0 otherwise




Table 4.11 Coefficients for main effects of the Thai model

Variable Coefficients SE 95% CI MO2 0.056 0.009 0.039 - 0.074 MO3 0.114 0.010 0.095 - 0.134 MO4 0.231 0.010 0.212 - 0.251 MO5 0.307 0.011 0.287 - 0.328

SC2 0.033 0.007 0.019 - 0.048 SC3 0.108 0.009 0.090 - 0.126 SC4 0.225 0.012 0.202 - 0.248 SC5 0.254 0.009 0.236 - 0.271

UA2 0.043 0.008 0.028 - 0.058 UA3 0.075 0.011 0.054 - 0.095 UA4 0.165 0.009 0.147 - 0.182 UA5 0.207 0.009 0.190 - 0.225


Table 4.11 Coefficients for main effects of the Thai model (cont.)

Variable Coefficients SE 95% CI

PD2 0.040 0.007 0.025 - 0.054 PD3 0.068 0.010 0.048 - 0.088 PD4 0.233 0.010 0.214 - 0.253 PD5 0.266 0.011 0.246 - 0.287

AD2 0.032 0.007 0.017 - 0.047 AD3 0.097 0.010 0.076 - 0.117 AD4 0.202 0.009 0.185 - 0.220 AD5 0.249 0.010 0.229 - 0.269

Table 4.12 Examples for calculating the Thai preference score for the EQ-5D-5L

Variable Coefficients The preference score

for health state 12112

The preference score for health state

55555 MO2 0.056

MO3 0.114 MO4 0.231 MO5 0.307

-0.307

SC2 0.033 -0.033 SC3 0.108

SC4 0.225 SC5 0.254

-0.254

UA2 0.043 UA3 0.075 UA4 0.165 UA5 0.207

-0.207

PD2 0.040 PD3 0.068 PD4 0.233 PD5 0.266

-0.266

AD2 0.032 -0.032 AD3 0.097

AD4 0.202 AD5 0.249

-0.249 The preference score = 1-0.033-0.032 =1-0.307-0.254-

0.207-0.266-0.249 = 0.935 = -0.283


According to the Thai algorithm, the level 5 of the mobility dimension

(unable to walk about) has the most impact to the utility decrement (coefficient = 0.307),

followed by extreme pain or discomfort (coefficient = 0.266). While the state that has

less impact to the utility decrement is slightly anxious or depressed and slight problems

washing or dressing myself (coefficient = 0.032 and 0.033). Since no constant in the

algorithm, the full health (11111) can be directly calculated from this algorithm without

assume to be equal to 1.00. The second best score is 0.968 for health state 11112 and the

worst score is -0.283 for health state 55555. The number of health state which the

preference score is lower than zero (worse than dead) is 126 health states (4.03%).

4.1.7 Comparing Thai preference score with the interim value sets

from mapping technique

The interim scoring for the Thai EQ-5D-5L was estimated by the EuroQol

group (33, 34) using mapping methodology while the 5L valuation study in Thailand

was on going. Figure 4.9 compares the preference score between those 2 value sets. The

Y-axis represents preference score and X-axis represents 3,125 health states sorted by

severity level and health profiles. It is clearly that the preference score from this study

was higher than the score from mapping. Paired T-test revealed that there was statistical

significant difference between those two sets (p-value < 0.001) and the mean difference

was 0.2. The interim scoring generated more states with negative scores than Thai value

sets (558 vs 126 states). No inconsistent score found among 3,125 states of both value

sets. The value sets generated from this study was better than mapping method in terms

of responsiveness (SRM = 1.75 vs 0.92).

Figure 4.9 Comparing EQ-5D-5L utility score obtaining from surveying and mapping

Health state

Preference score


Table 4.13 Comparing EQ-5D-5L value sets’ parameter between Thai’s and interim

scoring

Parameter Thai value sets Interim scoring Second best score (11112) 0.968 0.814 Worst score (55555) -0.283 -0.452 # negative score 126 (4.03%) 558 (17.86%) # inconsistent score among 3,125 states 0 0 SRM 1.75 0.92 Mean difference (paired t-test) 0.2 (p-value < 0.001)

4.1.8 Valuation by DCE

The mean time used by the respondent for valuing 1 pair of DCE task was

48 seconds (sd = 31.77). It is noted that each respondent valued 7 pairs of DCE tasks,

yielding 14 records per respondent: 7 preferred health states and 7 rejected health

states. Regarding data management of DCE data, 450 out of 16,898 records (2.7%)

were considered as low quality data, as shown in Table 4.14. The mean time of DCE

task among low quality data was 40 seconds (sd = 30), which was statistically

significantly shorter (p<0.001) than average time of all sample.

Table 4.14 Number of respondents that met the criteria for low DCE data quality

Criteria Number of records* 1. All responses were on the same side. 70

(5 respondents x 14 records) 2. The responses had particular order. 238

(17 respondents x 14 records) 3. The time spent was < 10 seconds/pair 88 4. More severe health state was preferred 86 * The number of records can be double counted.

Various models were tested with the data set, however only the important

results were shown in this report. Both conditional logit model (clogit) and random

effect logit model (xtlogit) were employed to both original and cleaned data set. Model

2 showed better fit statistics (in terms of pseudo R2 and AIC) than model 1 and 3

(Table 4.15). However, the specification test (linktest) revealed specification error

which usually means that there were omitted variables for model 1-3. Therefore the

interaction terms among 5 dimensions and 5 levels of severity (130 interaction terms)

were input in the model 2 and the results were showed in model 4. As a result, model 4


showed the best fit model while no misspecification was found. Nevertheless, all 4

models showed several inconsistent coefficients.

Table 4.15 Coefficients and fit statistics of DCE model

Functional form

Model 1 Model 2 Model 3 Model 4 conditional logistic

model conditional logistic

model (cleaned data) random effect logit

model (cleaned data)

conditional logistic model with interaction terms*

(cleaned data) Coef SE Coeff SE Coeff SE Coeff SE

Const - - - - 0.312 0.071 - - MO2 -0.066 0.050 -0.063 0.051 -0.061 0.051 -0.175 0.148 MO3 0.092 0.053 0.088 0.054 0.079 0.055 -0.040 0.230 MO4 -0.106 0.052 -0.104 0.053 -0.105 0.053 -0.197 0.196 MO5 -0.291 0.054 -0.288 0.055 -0.288 0.056 -0.332 0.137

SC2 0.012 0.053 0.009 0.054 0.017 0.053 -0.100 0.160 SC3 0.010 0.053 0.006 0.054 0.004 0.054 -0.034 0.211 SC4 -0.104 0.053 -0.106 0.054 -0.104 0.055 -0.224 0.219 SC5 -0.141 0.051 -0.148 0.052 -0.144 0.053 -0.164 0.134

UA2 -0.046 0.052 -0.052 0.053 -0.056 0.053 -0.012 0.172 UA3 0.066 0.055 0.058 0.056 0.043 0.055 0.066 0.203 UA4 -0.061 0.052 -0.066 0.053 -0.067 0.054 -0.027 0.237 UA5 -0.157 0.053 -0.161 0.054 -0.163 0.054 -0.190 0.131

PD2 0.045 0.052 0.047 0.053 0.034 0.053 -0.124 0.167 PD3 0.059 0.053 0.062 0.054 0.046 0.054 0.067 0.207 PD4 -0.100 0.052 -0.099 0.053 -0.110 0.054 -0.293 0.229 PD5 -0.191 0.053 -0.189 0.054 -0.198 0.055 -0.243 0.135

AD2 0.003 0.053 0.000 0.054 -0.003 0.053 0.040 0.150 AD3 -0.101 0.052 -0.101 0.053 -0.104 0.054 -0.069 0.211 AD4 -0.198 0.052 -0.195 0.053 -0.191 0.054 -0.218 0.194 AD5 -0.260 0.052 -0.257 0.053 -0.264 0.054 -0.184 0.133

Pseudo R2 12.64 12.91 n/a 21.19 AIC 17363 16696 20295 15291

* The coefficients of interaction term were not showed due to too many significant terms (61 out of the total 130 interaction terms).

With regard to DCE feedback questions, most of Thai respondents

understand the DCE questions; however they reported that it was difficult to

differentiate between 2 health states which they were asked (Table 4.16).

Table 4.16 DCE feedback

Questions Completely Agree Agree Neutral Disagree Completely

disagree Question 1 308 (25.52%) 670 (55.51%) 205 (16.98%) 23 (1.91%) 1 (0.08%)

Question 2 29 (2.40%) 194 (16.07%) 285 (23.61%) 509 (42.17%) 190 (15.74%)

Question 3 215 (17.81%) 641 (53.11%) 267 (22.12%) 79 (6.55%) 5 (0.41%) Question 1 : It was easy to understand the questions I was asked.

Question 2 : I found it easy to tell the difference between the health state I was asked to think about.

Question 3 : I found it difficult to decide on my answers to the questions.


4.1.9 Country-specific data

After completed TTO and DCE valuation, all respondents were asked with

5 questions designed by Thai researchers. These questions needed the respondents to

express their opinions about philosophy of life. They were asked whether they agreed

with the following sentences.

Sentence 1 - Everything that happens in my life is a consequence of my

actions from my previous life.

Sentence 2 - When I am sick, there is someone looks after me.

Sentence 3 - Regardless of any serious sickness, I try to live as long as

possible to have something done.

Sentence 4 - According to my belief, to escape the problems by

terminating my own life is seriously wrong.

Sentence 5 - I hold the religious doctrine when I face the problems in my

life.

Table 4.17 shows that a majority of the respondents (93%) hold the

religious doctrine when facing to the problems in their life; and 87% agreed that it was

seriously wrong to escape the problems by terminating their own life. Sixty three per

cent believed in a consequence of actions from the previous life. With regard to

sickness, 86% agreed that there was someone looking after them when they were sick;

and 75% expressed that they would try to live as long as possible to have something

done regardless of how serious the sickness.

Table 4.17 Opinions of the respondents towards philosophy of life (n=1,207)

Sentences Completely disagree Disagree Neutral Agree Completely

agree 1 - Everything that happens in my life is a consequence of my actions from my previous life.

49 (4.06%)

166 (13.75%)

233 (19.30%)

590 (48.88%)

169 (14.00%)

2 - When I am sick, there is someone looks after me.

12 (0.99%)

39 (3.23%)

115 (9.53%)

769 (63.71%)

272 (22.54%)

3 - Regardless of any serious sickness, I try to live as long as possible to have something done.

44 (3.65%)

148 (12.26%)

112 (9.28%)

595 (49.30%)

308 (25.52%)


Table 4.17 Opinions of the respondents towards philosophy of life (n=1,207) (cont.)

Sentences Completely disagree Disagree Neutral Agree Completely

agree 4 - According to my belief, to escape the problems by terminating my own life is seriously wrong.

30 (2.49%)

69 (5.72%)

63 (5.22%)

436 (36.12%)

609 (50.46%)

5 - I hold the religious doctrine when I face the problems in my life.

2 (0.17%)

16 (1.33%)

63 (5.22%)

777 (64.37%)

350 (29.00%)

4.1.10 Qualitative data

After completion of TTO, DCE valuation, and country-specific questions,

105 out of 1,207 respondents, who had unique responses to the TTO questions, were

asked with the following 3 questions. The first question asked the respondents about

the particular reasons to their TTO responses. The second question required the

respondents to choose 2 dimensions (the most important and the least important

dimension) from the 5 dimensions of the EQ-5D. In the third question, specific

scenario was described then the respondents were required to choose 1 preferred life

from 3 options. The details of each questions were described below.

First question

According to the first question, the respondents were further classified into

3 groups according to their reasons as follows:

Group 1: Respondents who preferred longer life even that life had a severe

health.

Group 2: Respondents, whose TTO response were based on only 1-2

health dimensions.

Group 3: Respondents who preferred full health even it offered a shorter

life.

A majority of respondents was classified in group 1 (63%) or those who

preferred longer life even that life suffered from severe health. Four main reasons

explained by these groups were as follow: 1) they firmly believed that they would get

well care and support from their families; 2) they had obligations or life goals to be


completed; 3) they had good experience on severe illness; and 4) they had certain belief

about human life. These 4 reasons could be further explained in more details below.

1.1 Care and support of family

Most respondents, who preferred longer life even that life was suffered

from a severe health, argued that if they were ill, they would get well care and support

from their family. The warm care and well support from family were important for one

to endure and struggle with severe illness. While they were encouraged from their

family, they gain more spirit to fight against the illness. At the same time, their

attempts to fight with the sickness showed good example for their family members,

and also encouraged family members to be vigorous. On the other hand, if they felt

discouraged and gave up living, their family members were also discouraged.

1.2 Obligations or Life Goals

Respondents who were the head of family often argued that they must live

as long as possible to be a pillar of their family. They had some obligations to be

complete. For example, they had to earn for their family or children; they had to took

care of aging parents; and they had to took care a sibling with illness or disability.

Some respondents had life goal to be completed before death. For instance, they

wanted to look after their parents in return; they wanted to live longer with their

lovers; they wanted to do something that they had never done before. The attitude

towards life of the respondents from this group was that the life was still valuable

despite having a severe illness. They expressed that even they got illness; they could

still make positive contribution to society unlike death people who could never do

anything to anyone.

1.3 Good experience on illness

The good experience on illness could be either a direct or an indirect

experience. In terms of direct experience on illness, some respondents had experience

on a serious illness and almost died once before. However, they could fight and

survive ultimately. So they believed that if they face serious illness again, they could

fight, survive and live long again. In terms of indirect experience on illness, some

respondents had experiences in caring patients/parents/relatives who had severe

illness. They saw that those ill people still fight and can live with the illness. Those ill

people became their role model or inspiration for the respondents to struggle with

illness; and also caused the respondents realized that life is valuable.


1.4 Belief about human life

Some respondents gave a reason on certain belief about human life. They

indicated very interesting reason that to be born as a human was very difficult. So it’s

worth to spend life as long as possible. They hoped to perform merit or contribute to

society as much as possible. They believed that it was uncertainty if they would have

the opportunity to be born as a human again in the next life. This belief reflected the

Buddhist lifestyle among Thai society.

There were 19% of respondents classified into group 2 (the respondents

whose TTO responses were based only on 1-2 health dimensions). The most important

health dimensions for them were anxiety/depression, mobility, and pain/discomfort,

respectively. The respondents who prioritized the anxiety/depression the first indicated

that the anxiety/depression made them desperate, disheartened, unhappy, and it could

become the root cause of other health problems. These symptoms were more difficult

to be treated than physical symptoms. In terms of mobility, the respondents expressed

that the problems in movement may trouble most activities in their life. They felt that

they were disabled and become a burden of their family. The respondents who mainly

make decision with pain/discomfort dimension thought that pain/discomfort was the

most suffering symptoms among all 5 dimensions; and it was too hard to bear.

There were 19% of respondents classified into group 3 (the respondents

preferred full health; even it offered a shorter life.) The main reasons were that the sick

people were burden of the family. The respondents aged between 18-29 years old

argued that they were still young, healthy and could do anything by themselves. If they

get sick for long time, they might be unable to accept it since sick people is recognized

as a burden of a family. Also the rest of their life would be filled with suffering and

unhappy. Whereas, the respondents aged over 60 years gave the meaning of sick

people that it was the person who was unable to take care of themselves and was a

burden of family and peers. If they were sick and had no one look after them; their life

would be difficult. They might be seen as disabled, no value, and could not afford their

family. The rest of life would be meaningless.


Second question

The second question needed the respondents choose 2 dimensions (the

most important problem and the least important problem) from the 5 dimensions of the

EQ-5D. Table 4.18 showed the most important and least important dimension

specified by the respondents. Many respondents (40%) expressed that the most

important problem was unable to walk and the least important problem was unable to

do usual activities (23%) and extremely anxious or depressed (23%).

Table 4.18 Opinions of the respondents on the most and the least important dimension

(n=105)

Dimension Most important Least important

1. unable to walk about 42 (40%) 23 (22%)

2. unable to wash or dress myself 3 (3%) 20 (19%)

3. unable to do my usual activities 9 (9%) 24 (23%)

4. extreme pain or discomfort 24 (23%) 14 (13%)

5. extremely anxious or depressed 27 (26%) 24 (23%)

Third question

In the third question, the following scenario was given: "If you were in

severe health e.g. last stage cancer, you were suffered from severe pain, and that you

will die soon". Which option would you prefer?

Option 1: Being treated with medicine without charge. This medication

would extend the life for 5 years but in severe health condition and severe pain.

Option 2: Being treated with medicine without charge. This medication

would extend the life for 6 months with full health (no pain).

Option 3: Giving up living without any treatment.

Half of respondents (54%) chose option 2 which preferred full health even

their life was shorter. The key reason was that they had a bad experience with cancer.

They had ever seen their child, parents, relatives, or neighbors died from cancer. Prior

to death, those people suffered from the painfulness of chemotherapy as well as

symptoms of cancer. These produced bad impression and negative attitude towards

cancer. With a healthy life, even short time (6 months), they could do all activities,


e.g. earning for family; giving child the parting instruction; calm their life down and

prepare to death.

Twenty nine percent chose option 1 which preferred long life with severe

pain. The main reasons were worrying about family; positive thinking; and good

experiences towards cancer, respectively. Some respondents argued that they wanted

to stay with their family as long as possible to see their children grow and succeed in

life. In addition, they believed that their family would take care of and encourage them

to fight against serious illness. Some respondents had positive thinking and optimism.

They argued that the illness was a simple matter of life which no one in this world

could escape from this truth. Some respondents had good experience in caring patient

with serious illness; so they understand well about suffering from serious illness. They

believed that if they had serious illness, they could fight and endure the pain to live as

long as possible.

Eleven percent chose option 3, which was giving up their lives. Some

respondents had negative attitude towards serious illness. They had ever seen a patient

suffered from serious illness, and finally died. Some respondents did not want to be a

burden to family. They expressed that if they had severe pain, their family would

suffer also. If they died, they would not be a burden to anyone anymore. Some

respondents mentioned about the truth of life that for all 3 options of life, finally

everybody dies. So it was no meaning to try to live longer with sufferings. Thus these

respondents chose to give up living without any treatment to eliminate all problems

and stop their own and family’s sufferings.

Session 4.2: Testing the measurement properties of the Thai version

of the 5L compared to the 3L

4.2.1 Characteristics of respondents

A total of 117 patients with diabetes mellitus who met the eligibility

criteria were included. The characteristics of the respondents are shown in Table 4.19.

The average age of the respondents was 45 years, with 62.4% being female. Sixty-four

(54.7%) respondents had type 2 diabetes. The average diabetes duration of the sample


was 9 years and the average BMI was 23.30. Of the 117 respondents who completed the

first survey, 101 respondents (86%) returned the second questionnaire set by postal mail.

Table 4.19 Demographic characteristic of respondents

Demographic characteristic n (%) Type of diabetes

Type 1 53 (45.3) Type 2 64 (54.7)

Gender Male 44 (37.6) Female 73 (62.4)

Marital status Single 58 (49.6) Married 46 (39.3) Widowed 9 (7.7) Divorced/Separated 4 (3.4)

Education High school 51 (43.6) Primary school 27 (23.1) Bachelor’s degree 25 (21.4) Diploma 10 (8.5) Master’s degree or higher 4 (3.4)

Occupation Student 50 (42.7) Government/state enterprise officer 20 (17.1) Housewife 14 (12.0) Business owner 11 (9.4) Unskilled labor 7 (6.0) Retired 6 (5.1) Employee 4 (3.4) Agriculture/fishery 2 (1.7) Other 3 (2.6)

Health insurance Civil Servants Medical Benefits Scheme 58 (49.6) Out of pocket 32 (27.4) Universal coverage 20 (17.1) Social security 7 (6.0)

Median (IQR) Age (years) 45.00 (40.0) Diabetes duration (yr) 9.00 (8.50) BMI (kg/m2) 23.30 (7.37) Household income per month (Baht) 30,000 (30,000)


The health state ‘11111’ was observed in 29.1% in the 5L and 33.3% for

the 3L. The second-most frequent health state reported was ‘11121’ which was 14.5%

in the 5L and 15.4% in the 3L. There were no missing values from both the 5L and

the 3L, indicating good feasibility for both instruments.

4.2.2 Distribution and ceiling effect

For all dimensions, most respondents reported no problems (level 1) for

both the 3L (52-98%) and the 5L (44-97%), as shown in Figure 4.10. Among

responses with health problems, it was clear that the 5L demonstrated better severity

level distribution than the 3L except for self-care.

With regards to the ceiling effect, the 5L showed a slightly decreasing

trend for no problem responses compared with the 3L. The percentage of patients

reporting the health state ‘11111’ decreased from 33% in the 3L to 29% in the 5L.

Nevertheless, no statistically significant difference was found. Self-care reached the

highest ceiling effect (98% for the 3L, 97% for the 5L) and showed the smallest

reduction in ceiling effect (1%) with the 5L. In contrast, pain/discomfort showed the

smallest ceiling effect (52% for the 3L, 44% for the 5L) and also showed statistically

significant reduction in ceiling effect with the 5L. No statistically significant reduction

was found for the other dimensions.

Figure 4.10 Distribution across severity level of the 3L and 5L dimension

(MO = Mobility; SC = Self-care; UA = Usual activities; PD = Pain/discomfort; AD = Anxiety/depression; No level 3 reported for the 3L and no level 5 reported for the 5L)

% of respondent


4.2.3 Redistribution

Among the answers of no problem (level 1) on the 3L, most of them

(85-98%) remained the same (no problem) on the 5L while 2-15% redistributed to

slight problems (level 2) on the 5L, as shown in Table 4.20. The majority of the

respondents who reported moderate problems (level 2) on the 3L indicated slight

problems (level 2) on the 5L (69-100%), while 9-22% shifted to moderate problems

(level 3) on the 5L. As such, redistribution occurred the least in self-care. The mean

VAS score tended to be lower according to the severity level of the 5L. No

inconsistent response was found in this study.

Table 4.20 Redistribution pattern of response from the 3L to the 5L

Dimension 3L 5L n (%) Mean VAS

Size of inconsistent response *

Mobility 1

1 83 (98%) 81.02 -1

2 2 (2%) 85.00 0

2

2 22 (69%) 72.38 0

3 7 (22%) 71.43 -1

4 3 (9%) 72.67 0

Self-care 1

1 113 (98%) 79.19 -1

2 2 (2%) 70.00 0

2 2 2 (100%) 60.00 0

Usual activities 1

1 93 (98%) 80.82 -1

2 2 (2%) 80.00 0

2

2 20 (91%) 71.85 0

3 2 (9%) 50.00 -1

Pain/discomfort 1

1 52 (85%) 81.54 -1

2 9 (15%) 86.33 0

2

2 45 (80%) 77.77 0

3 10 (18%) 64.50 -1

4 1 (2%) 50.00 0

Anxiety/depression 1

1 84 (94%) 81.38 -1

2 5 (6%) 71.80 0

2

2 23 (82%) 73.48 0

3 4 (14%) 67.50 -1

4 1 (4%) 60.00 0

* The size of inconsistency of < 0 indicated consistency.


4.2.4 Convergent validity

Table 4.21 demonstrates the Spearman’s correlation coefficients between

the EQ-5D and SF-36v2 dimensions. The relation among similar dimensions was

statistically significant. Both the 3L and 5L showed an acceptable degree of

association with the SF-36v2 except for self-care, where the degree of association was

relatively low. A strong association was found between mobility and physical

functioning for both the 3L and 5L (r = 0.54 for the 3L, r = 0.53 for the 5L). Mobility

was also moderately associated with bodily pain and general health perception. With

regards to self-care, the 5L had weak association with physical functioning and social

functioning while no association was found between the 3L and SF-36v2. The usual

activities of both the 3L and the 5L were weakly associated with various dimensions

of SF-36v2, i.e. bodily pain, physical functioning, and mental health. The association

between pain/discomfort and bodily pain improved in the 5L (r = 0.30 for the 3L, r =

0.35 for the 5L) and the association between anxiety/depression and mental health was

greater in the 5L as well (r = 0.45 for the 3L, r = 0.49 for the 5L). Additionally,

Pearson’s correlation coefficient between the VAS score and utility index was similar

between the 3L and 5L (0.36 for the 3L, 0.35 for the 5L with p-value < 0.001).

Table 4.21 Correlation coefficients between EQ-5D and SF-36v2 dimensions

Dimension PF RP BP GH VT SF RE MH

3L

Mobility .54** .28** .41** .42** .25** -0.07 0.11 0.14

Self-care 0.16 0.05 .19* 0.12 0.14 0.16 0.06 0.18

Usual activities .25** .21* .30** .19* .27** 0.18 0.13 .28**

Pain/discomfort .19* 0.17 .30** .24** .18* 0.11 .21* .22*

Anxiety/depression 0.05 0.09 .23* .22* .21* .32** .29** .45**

5L

Mobility .53** .29** .44** .44** .23* -0.08 0.09 0.11

Self-care .24** .20* .23* 0.18 0.16 .24** .21* .22*

Usual activities .30** .23* .29** .22* .24* 0.16 0.14 .24**

Pain/discomfort .24** .23* .35** .28** .22* 0.08 0.16 0.18

Anxiety/depression 0.08 0.12 .19* .21* .28** .35** .29** .49** PF (physical functioning), RP (role limitation due to physical problems), BP (bodily pain), GH (general health perceptions), SF (social functioning), VT (vitality), RE (role limitations due to emotional problems), MH (general mental health) * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).


4.2.5 Discriminative power

The absolute informativity ( ) of the 5L was higher than the 3L for all

dimensions as shown in Table 4.22. This reflected that the 5L generated more

informativity than the 3L. We also found that the 5L generated similar results

compared with the 3L when it came to relative informativity ( ).


Table 4.22 Shannon index ( ) and Shannon’s Evenness index ( ) of the 3L and the 5L

Dimension

3L 5L

3L 5L Mobility 0.85 1.20

0.53 0.52

Self-care 0.12 0.21

0.08 0.09 Usual activities 0.70 0.78

0.44 0.34

Pain/discomfort 1.00 1.40

0.63 0.60 Anxiety/depression 0.79 1.06

0.50 0.46

4.2.6 Test-retest reliability

The time interval between the first and second test was approximately 3

weeks. Overall, the reliability coefficient and percentage agreement of the 5L were

slightly lower than the 3L (Table 4.23). The weighted kappa coefficient for the 3L

ranged between 0.39 and 0.70, and between 0.44 and 0.57 for the 5L; this indicated

that the 3L had better reproducibility than the 5L. The percentage agreement gave

higher values than the weighted kappa coefficient; it was between 0.78 and 0.98 for

the 3L and 0.67 and 0.97 for the 5L. The ICCs of the 3L and 5L indexes were 0.64

and 0.70, respectively, which indicated excellent reproducibility for both instruments.

Table 4.23 Test-retest reliability of the 3L and the 5L

Dimension Weighted kappa coefficient (95% CI) Percentage

agreement 3L 5L 3L 5L

Mobility 0.70 (0.53-0.86) 0.57 (0.40-0.74) 0.89 0.83 Self-care n/a* n/a* 0.98 0.97 Usual activities 0.39 (0.16-0.62) 0.45 (0.25-0.65) 0.82 0.81 Pain/discomfort 0.56 (0.39-0.72) 0.44 (0.29-0.58) 0.78 0.67 Anxiety/depression 0.50 (0.31-0.70) 0.49 (0.33-0.65) 0.82 0.77

Intraclass correlation coefficient ** EQ-5D index 0.64 (0.51-0.74) 0.70 (0.57-0.79)

* not enough information to calculate kappa coefficient for self-care dimension. ** ICC was 2-way random, single measures, and absolute agreement.

4.2.7 Coefficient of variation

The coefficient of variation (CV) was calculated from all respondents

(excluded the respondents who reported perfect health), since the majority of

respondents of this study had mild condition and the sample size was small, Figure 4.11


demonstrated that the preference score produced by 5L has lower variation than 3L

although standard deviations were similar.

Figure 4.11 Mean, standard deviation, and coefficient variation of preference score

4.2.8 Patient preferences

Thirty-six percent of respondents indicated that the 5L was easier to

answer than the 3L while 33% of respondents indicated that there was no difference

between the 5L and the 3L. In terms of reflecting health status, most respondents

(63%) agreed that the 5L was better in describing their health states while 29%

indicated that both versions were similar.



This session showed the economic evaluation results generated from 3

different value sets (Thai population-based 3L value set, 5L value set generated from

mapping, and Thai population-based value set of the 5L generated in session 1). The

results of both DM type 1 and DM type 2 were similar. The mean preference scores

produced from Thai population-based 3L value set was similar to those of the 5L value

set generated from mapping, however both of them were lower than the population-

based preferences of the 5L generated from this study. Thus the QALY calculated


from the 5L value set derived from this study was higher than those generated from the

Thai population-based 3L and the 5L value sets from mapping, as shown in Table 4.24.

The ICER, which was calculated by incremental cost divided by

incremental QALY, was derived using the probabilistic determination by fixing all

parameters accept for utility. The results showed that the scores generated from this

study yielded lower ICER than those generated from the Thai population-based 3L

value set and those generated from the 5L by mapping technique. Thus it implied that

the economic evaluation model which used utilities generated by Thai 5L population-

based value set was likely to be more cost-effective than those used the utilities

generated by the Thai population-based 3L value set and the 5L value set generated

from mapping.

Table 4.24 Economic evaluation results generated from 3 different value sets

Tariffs Mean utility

Standard error

∆ cost ∆ QALY ICER *

(Baht/QALY gained)

DM1 - Population-based 3L

value set 0.8323 0.0202 276,934 5.55 49,898

- 5L value set (mapping) 0.8335 0.0176 276,372 5.55 49,835 - Population-based 5L

value set 0.9597 0.0051 276,278 6.38 43,322

DM2 - Population-based 3L

value set 0.7308 0.0222 123,968 0.29 431,484

- 5L value set (mapping) 0.7366 0.0219 119,806 0.29 414,965 - Population-based 5L

value set 0.8991 0.0130 124,387 0.36 349,918

* ICER = ∆ cost / ∆ QALY

The sensitivity analysis of economic modeling was presented in cost-

effectiveness plane which was a scatter plot of the bootstrapped incremental costs and

effect (QALY) pairs. It was used to illustrate the uncertainty surrounding the expected

costs and expected effects. Since the utility produced from 5L value sets (mapping)

was similar to population-based 3L value set, only the results from population-based

3L value set and population-based 5L value set were demonstrated. The dots were

plotted by randomly input parameters into the model one thousand and five hundred


times repeatedly. In order to clearly demonstrate the effect of utility produced by 2

different value sets, utility values were varied to the possible range while all other

variables were fixed.

Figure 4.12 shows the cost-effectiveness plane which the solid line

represents the threshold for Thailand (160,000 Baht per QALY). The graph showed

that the population-based 5L value set produced less steep slope than population-based

3L value set. Thus using population-based 5L value set in economic model yielded

better value for money than population-based 3L value set.

Figure 4.12 Cost-effectiveness plane comparing SMBG group to no SMBG group for

DM type 1 & 2

3L

5L

3L 5L

Threshold =160,000 Baht/QALY

Threshold =160,000 Baht/QALY

Incremental cost

Incremental QALY

Incremental cost

Incremental QALY

DM1

DM2


Figure 4.13 shows head-to-head comparison of preference score of

comparable health states between 3L and 5L. It is clearly that the preference score

produces by 5L is higher than 3L for all comparable health states. This evidence

confirm that using Thai 5L tariff in economic model is likely to yield better value for

money than Thai 3L tariff.

Figure 4.13 Preference score for comparable health states of the 3L and the 5L

The cost-effectiveness acceptability curve (CEAC) was used to summarize

the information on uncertainty in cost-effectiveness analysis. It was drawn by plotting

the proportion of the costs and QALY pairs that were cost-effective for the maximum

acceptable ceiling ratio. Figure 4.14 and 4.15 shows the probability that the intervention

group (SMBG) or reference group (no SMBG) was cost-effective. The CEAC generated

by population-based 5L value set showed steeper than population-based 3L value set,

especially among DM type 1. It implied that CEAC generated by population-based 5L

value set offered more certainty than population-based 3L value set. As a result, it was

easier for policy makers to make decision with the population-based 5L value set.

Preference score

Health state


Figure 4.14 Cost-effectiveness acceptability curve for DM type 1 group

3L

5L


Figure 4.15 Cost-effectiveness acceptability curve for DM type 2 group

3L

5L


CHAPTER V

DISCUSSIONS


scores for the 5L

5.1.1 Sampling and characteristic of sample

To ensure representativeness for Thai population, various steps were

employed. The study areas were selected using probabilistic sampling by the NSO.

This technique reduced both systematic and sampling bias as every area in Thailand

had an equal opportunity for selection. Regarding individual selection, quota sampling

by age and sex was undertaken in order to obtain the sample that similar to Thai

population structure. Otherwise, most of respondents may be elderly because most of

young labors migrated to work in the big city. However, the sample in our study may

still not be a representative of Thai population due to non-response of some

respondents or oversampling of certain population. Thus, prior to analyze, the data was

weighted by the NSO to correct for non-representativeness. The sample of this study

was therefore representing the Thai population.

Regarding health status of the sample, most of them were healthy as mean

VAS score was 83, 30% reported perfect health, and less than 10% reported moderate

problem or severe problems. However, it was noticed that 44% of sample reported

having slight pain/discomfort (level 2). This may due to the fact that most of the

respondents (54%) worked in agriculture sector or unskilled labor which might cause

them fatigue. Compared with the previous valuation study for 3L in Thailand (11, 12),

63% of respondent also reported moderate pain/discomfort (level 2). This was also

consistent with the responses in session 2, which found that 46% of respondent

reported slight problems for pain in the 5L.

Juntana Pattanaphesaj Discussions / 104

5.1.2 Methodology

5.1.2.1 TTO valuation

The TTO valuation used to elicit population-based preferences

for the 5L was different from the 3L in many ways. Firstly, the 3L valuation in

Thailand (11, 12) used TTO board which was adapted from the props used in the

MVH protocol (94) to elicit health preference. One side of the TTO board was used

for valuation health states that the respondents considered as better than death while

the other side was used for health states viewed as worse than death. This tool was

difficult and inconvenience for use as interviewers had to change the year in full health

by themselves. In addition, this technique was more prone to human error. On the

other hand, the 5L valuation followed the EQ-VT protocol (73) which was a face-to-

face setting and used a laptop installed with the Thai version of ‘EQ-VT offline’ to

interview. The advantage of using digital technology in data collection was that it was

less burden to the interviewers. The missing values were also absent as the computer

program did not allowed to skip the questions. Nevertheless, a few missing records

occurred due to technical problems in software and/or internet connection during

upload the data to central server and update the software to the new version. Another

advantage was that it can reduce burden and error in data entry process. In addition,

using a touch screen laptop in data collecting could help the respondents enjoy

interview in the almost 1-hour interviewing per respondent. In the Thai context, the

respondents had various characteristics. Most respondents can use a touch screen

laptop skillfully. However, some respondents scared to touch it. In this case, the

interviewer demonstrated how to touch the screen at the earliest stage of interview and

then let the respondents touch the screen for the answers by themselves.

Secondly, in this study (5L valuation) the value of worse than

dead health state was elicited using lead-time TTO (51) which allowed both better than

death and worse than death health state to be valued with the same valuation technique

and concept. The purpose of adding lead time in full health to both two alternatives

(life A and life B) was to keep the utility calculation to be the same. It allowed

respondents to trade their lead time to avoid worse than death health state. Since

different lead time duration yielded different results, the appropriate length of lead

time was studies in many countries (51, 102, 103). A 10-year of lead time used in this


study would make possible comparison with the MVH value set elicited in the same

duration. On the other hand, the 3L valuation used the conventional TTO valuation

(MVH protocol) for worse than death state. In the conventional TTO valuation,

different trade-offs tasks and also different concept in utility calculation was used for

better than death and worse than death health state. As a results, MVH elicitation

produced an extreme negative values and needed transformation to bound the negative

value to -1 (104).

According to the EQ-VT protocol, the number of health state

used to elicit preference for 5L was 86 states out of the total 3,125 states, accounted

for 2.75%. While MVH protocol for the 3L valuation offered 42 health states (17%)

out of 243 states. The pilot study (74) demonstrated that these 86 selected health states

for 5L valuation were sufficient and efficient to predict 3,125 health states. However,

it was found that some selected health state were difficult to imagine for Thai, for

example state 51152 which the one was unable to walk but he/she had no problem in

self-care and usual activities. The selected health states which were difficult to

imagine may distort the TTO score and lead to inconsistent coefficients in the model.

The health states used to elicit preference should not just cover the full range of

severity; but also they should close to the reality.

Regarding the method used to determine the logical

inconsistency among 10 health states for each respondent in the TTO valuation, Devlin

et al. determined the logical inconsistency by counting the number of pair

inconsistency pairs (75) according to the methods proposed by Dolan and Kind (77).

In Devlin et al, the respondents were divided into 8 groups according to the degree of

inconsistency. It was found that the actual mean scores of each subgroup were not

statistically different from the subgroup with zero inconsistency. Thus Devlin

recommended estimating the tariff from the full sample. Differently, this study

determined the inconsistency by calculating the magnitude of inconsistency. The

inconsistency existed when a higher value was assigned to a worse health state (more

severity level). This method was different from Dolan and Kind (77) method.

According to Dolan and Kind (77), state A was logically worse than state B when all

level of state A was equal or worse than state B. If some levels of state A were worse

while some were better than state B, these two states can’t be compared. In addition,


by using magnitude of inconsistency to exclude low quality data (41 respondents) we

found that the cross validation and fit statistics in the regression model were improved.

This study employed multilevel modeling for estimating Thai

value sets because the data set was panel data which 1 respondent were 10 times

repeated measured. Heteroscedasticity is common for panel data (81). The multilevel

modeling is more flexible with this kind of data as it can represent regression models

where the residual variance is not constant (81). The OLS was not appropriate for our

data because it might produce bias and misleading parameter estimates. This is

because one important assumption of OLS is homoscedasticity of residuals. Thus

heteroscedasticity of our data can be very problematic with OLS.

This study did not found the inconsistency of the coefficients

in regression model produced from TTO valuation. This may be due to the high

quality in data collection process. In our study, all 6 interviewers were well-trained.

The pilot study also allowed them to practice with the total of 100 respondents in the 8

settings. Importantly, regularly data uploading to a central server of the EuroQol

group and the quality control tool, which was an excel program developed by the

EuroQol group, allowed principal investigator (PI) to know all details of interviewing

real time, i.e. interviewing time, logical inconsistence responses. As the result, the PI

can notice the problems at the early step, which can help improving the quality of data

collection in due time. Nevertheless, the EQ-VT could not yet promptly identify the

logical inconsistency during interviewing.

5.1.2.2 DCE valuation

At the present, there is no standard protocol for calculating the

data from DCE valuation. Previous studies showed the feasible (105, 106) and

advantages of DCE over TTO valuation (90). The DCE allowed fewer data exclusions.

This is important when the representativeness of population was concerned. On the

other hand, TTO studies needs well data management as some respondents might not

understand the task. However, the main difficulty in analyzing DCE data was that the

values generated from the regression model were on arbitrary scale which needed

some methodology to anchor the values derived from DCE on the QALY scale (74,

91, 105). Two strategies for anchoring were employed (74, 91). The first method used


the worst value estimated from lead-time TTO model (55555) to anchor for the worst

state of DCE. The second method anchored on death state, which need more DCE

question, i.e. whether life A was WTD and whether life B was WTD. The DCE

questions from EQ-VT protocol had no such questions, so only first method can be

undertaken.

The inconsistent coefficients found in this study was similar to

those found in the Spanish (91) and English report (this conference material was not

allowed to cite). Although DCE valuation was claimed as easier than TTO valuation

(90, 106) which the respondents may not understand the task, difficulty of making

choices on prefer health state was found. Feedback questions reported that 81% of

Thai respondent understood the DCE task, however 71% of Thai respondents found

that it was difficult to choose preferred life on the DCE task. On the other hand, 62%

of Thai respondent indicated that they understood the TTO task, and 62% reported that

it was difficult to decide on the exact point where two lives were about the same. This

may implied that DCE task was more difficult on deciding the preferred life than TTO

task for Thai respondents. This finding was similar to Spanish report (91) which many

respondents did comment on the difficulty of making choices between health states.

This might because DCE task needed the respondents to imagine and compare 2 health

states with problems at the same time, while it might easier for Thai respondents to

imagine 1 health state with problems compared to perfect health in TTO task. During

interview, it was noticed that some respondents made decision on DCE task very

quick, i.e. they considered only whether the problems on a particular dimension

existed or not; and some of them did not consider the severity level of dimension.

5.1.3 Preference score

This study suggested the 5L algorithm without a constant or interaction

terms to estimate preference score for Thai population. It is interesting to compare the

5L value sets with those of other countries, unfortunately, the official publications on

the 5L valuation were not found. Comparing with the interim scoring generated by the

EuroQol group using mapping method (33, 34), the evidences demonstrated that the

predicted score of Thai population-based value sets for was statistically significant

different from the interim scoring. This might be due to the fact that the sample of


interim scoring study was derived from 6 western countries although the Thai value set

of 3L was used to estimate interim value sets. Different context (i.e. culture, belief,

health behavior) between western and eastern could limit the validity of the value sets

estimated. One limitation of the mapping method was the floor effect as the worst state

of mapping method was anchored with the worst state of Thai value set for 3L (state

33333). As the result, it did not allow the value of state 55555 to be worse than state

33333.

The algorithm of this study was consistent with the Thai algorithm for the

3L (11, 12), and Brazilian study (107) which the smallest disutility was found in the

slightly (level 2) anxiety/depression dimension (0.032 in both the 3L and 5L, 0.062 for

Brazilian study). The largest disutility was found in the extreme problem (level 5) in

mobility dimension (0.432 for the 3L, 0.307 for the 5L, 0.40 for Brazilian study). The

greatest disutility was conformed to the opinion of the respondents that ‘unable to walk

about’ was the worst state. Since the 5L is more sensitive to mobility dimension, health

conditions which affect mobility could make great impact on the change of preference

score. However, considering health profile by each dimension is still needed (20).

With regard to the preference score of the 5L compared with 3L, the

second best state was 11112 (0.766 for the 3L, and 0.968 for the 5L). The score of the

worst health state of 3L and 5L was -0.454 and -0.283, respectively. The range of

value of the 5L was narrower than the 3L. It implied that using the 5L to measure

utility for the treatment of very severe conditions may resulting in lower QALY gains

than using the 3L. The differences in the range of score might mainly result from

different protocol used to elicit preference; especially the method used to elicit and

calculate WTD state. The current method used EQ-VT protocol which offered 10

years of full health as lead time for WTD state in order to keep the utility elicitation

and calculation to be the same as BTD state. However, the 3L valuation used MVH

protocol which produced an extreme negative values and needed transformation to

bound the negative value to -1 (104).

5.1.4 Limitations and future research

Sampling bias could occur when individuals were selected to be

interviewed. Although stratified-three stage sampling was undertaken, NSO employed

probability sampling for only first (provinces) and second stage (EAs). The third stage


(individuals) was selected by quota sampling which was non-probability sampling.

The quota sampling allowed the number of eligible participant with age and sex to

conform to the Thai population structure. The coordinators (mainly were the officer

from primary care unit) recruited participants according to the number of sex and age

range specified by the researcher. With this method, some members of the population

were less likely to be included than others resulting in a biased sample. Due to limitation

in budget and time, this study could not employ random sampling for individual.

The magnitude of inconsistency which was used as an indicator to

consider low quality data for 10 health states of TTO task may be crude and not

conform to Dolan and Kind method (76). These criteria determined the inconsistency

between health states from the level of severity. Nevertheless, the evidence

demonstrated that when the respondents with too high magnitude of inconsistency

(41 respondents) were excluded, the cross validation and fit statistics in the regression

model were improved.

The valuation study for the 5L using EQ-VT protocol consumed time and

budget as it needed a laptop for face-to-face interviewing. The research team also went

all over the country in order to get the data that could be a representative of Thai

population. The cost of travel, accommodation, and salary was substantial high.

Without financial support from funder, the valuation study was difficult to complete. It

also need well preparation, management, and good coordination with the community

leader or the officer at primary care unit.

The inconsistent coefficient found from DCE valuation needs the further

research to explore more about the cause of inconsistence and how to deal with these

problems.

Session 5.2: Testing the measurement properties of the Thai version

of the 5L compared to the 3L

This report is the first study in Thailand that assesses the measurement

properties of the 5L and compares it with the 3L. Similar to previous studies (22, 24,

26, 27, 29, 30), self-care showed the highest percentage of ceiling effect in both the 3L

and 5L. On the other hand, the lowest ceiling was found in pain/discomfort (44%) (24,


27, 29). Similar to the previous studies (22, 24-27, 29, 30), the proportion of the

ceiling effect in our study was lower in the 5L (29%) compared with the 3L (33%).

However, in the previous studies that involved patients with a variety of severity

higher reduction in ceiling effect of the 5L (3-17%) was identified (22, 24, 27, 29).

The smaller reduction in ceiling effects found in our study may be due to the fact that

our respondents were likely to be healthy (median VAS score = 78).

In each dimension, more than half of the responses were in level 1 (no

problem) for both the 3L and 5L. Among the respondents with health problems, it was

clearly shown that the 5L can present more details of severity than the 3L. While we

found that the majority of level 1 in the 3L still remained at level 1 in the 5L (85-98%),

about 2% (self-care) to 15% (in pain/discomfort) were upgraded to level 2 in the 5L.

The redistribution from 3L-level 2 (some problems) to 5L-level 2 (slight problems)

was also high, ranging from 69% for mobility to 100% for self-care, and the

redistribution from 3L-level 2 to 5L-level 3, ranging from 9% for usual activities to

22% for mobility. This finding supports the inclusion of the slight problems (level 2)

in the 5L. However, no supportive evidence of the inclusion of severe problems (level

4) in the 5L was found in our study as no 3L-level 3 responses were reported. This

may also be due to the fact that our respondents were likely to be healthy.

No inconsistent responses were found in our study. This indicates that our

respondents were able to consistently answer both the 3L and 5L. This is similar to

previous studies (21, 24, 26, 27, 29, 30) which showed that inconsistency was quite

low, ranging from 0.5% to 3.5%. However, the consistent responses may be due to the

low number of the sample size and the characteristics of our sample - educated and

healthy diabetic patients. In addition, even when the respondents completed the

questionnaires themselves, they were well-advised by trained staff.

The measurement of reliability and agreement is important in health

classification as it reveals the amount of errors of the measurement. The concept of

‘reliability’ differs from ‘agreement’ in that reliability is a relative measure which is

the ratio of variability between subjects to the total variability of all measurement in

the sample (100). Thus, it reflects the ability of an instrument to differentiate between

subjects. In contrast, an agreement is an absolute measure which is the degree to which

responses are identical. Cohen’s weighted kappa is often used in assessing test-retest


reliability of ordinal instruments as it takes the chance agreement into account.

However, the lack of variance in the data set meant that the kappa could not be

calculated so it was necessary to rely on the percentage agreement values. However, it

should be cautioned that the percentage agreement may give higher reproducibility

figures than the kappa coefficient (99). Unlike previous studies (27, 29, 30), our results

of the test-retest reliability/agreement showed that the 5L was slightly less

reproducible than the 3L in all dimensions. This is probably due to the fact that the

average time interval between the two tests was too long (3 weeks), and therefore the

different environment of repeated measurements may have impacted the answers

(100). In addition, the 5L’s ability to better capture small changes in health status may

have produced slightly less reproducibility. It should also be noted that the respondents

completed the first set of questionnaires by themselves at the hospital and repeated it

again at their home for the second session.

Convergent validity was evaluated by correlations between the EQ-5D and

SF-36v2 dimensions. Both the 3L and 5L presented an acceptable degree of

association and similar correlation pattern with the SF-36v2 in some pairs of

dimension, i.e. mobility versus physical functioning; pain/discomfort versus bodily

pain; and anxiety/depression versus mental health. The findings were similar to the

study by Kimman et al (67) that assessed the relationship of the 3L with the SF-36v2

among the occupational population in Thailand.

Similar to previous studies (21, 22, 26), absolute informativity ( )

increased in all dimensions for the 5L while in terms of the evenness of distribution

evaluated by Shannon’s Evenness index ( ), the 5L was comparable to the 3L. While

the maximum value of for the 5L is 2.32, our values ranged from 0.21 to 1.40

which was lower than the findings from Pickard et al (22) (0.84-2.00) and Janssen et al

(21) (2.05-2.26). With the maximum value of set at 1.00, our values ranged from

0.09 to 0.60 which was also lower than Pickard et al (22) (0.36-0.86) and Janssen et al

(21) (0.88-0.97). The lower and values found in our study may have risen from

the mild characteristic of our sample since the extreme problems (3L-level 3 and 5L-

level 5) were not reported. As the result, the levels of responses of the EQ-5D were

used ineffectively, resulting in low and values.


Although 5L offers 5 options to choose which seem to be more difficult to

make decision compared to 3L which offers only 3 options, the findings revealed that

Thai respondents preferred 5L than 3L as it was easier to use. Some of them expressed

that 3 options of 3L were crude, for example level 1 (no problem) was too good and

level 2 (moderate problems) was too bad to reflect their health. They would like to

choose in-between level (between level 1 and 2). Unfortunately no that option in 3L,

so it was hard to decide between level 1 and level 2 for 3L. Five options of 5L made

decision easier as the 5 choices offered were exactly reflect their health.

In our study, diabetic mellitus was chosen as it is a common chronic

disease that substantial affects quality of life (108, 109). Additionally, diabetes was

ranked as third and eighth in terms of Disability Adjusted Life Year (DALY) loss in

Thai women and men, respectively (96).We included patients with no complications in

our study to ensure that the health status will be stable enough in order to test the test-

retest reliability/agreement. However, given the mild condition of our sample, we

were unable to assess the redistribution of answers from the 3L-level 3 to the 5L.

Further studies should be conducted for patients with a variety of severe health

problems. In addition, it should be noted that the general findings of different groups

of patients should be made with caution as the pattern of responses may differ by

disease characteristics (14).

Regarding time to complete EQ-5D version, the methodology of this study did

not design to compare time to complete 3L and 5L. However the average time to complete 2

versions of EQ-5D (3L and 5L) was 4 minutes. Thus, the time used to complete 1 version of

EQ-5D by themselves for Thai respondents may less than 4 minutes.

One more limitation is that the 5L utility score was obtained from the interim

mapping generated by the EuroQol group since the valuation study for the 5L in Thailand

has not been completed yet. Although the calculation was based on the Thai 3L value sets,

the results of the mapping may deviate compared to the actual responses (66).



This study showed the results from only one economic modeling which

used the preference score derived from the Thai 3L value set and the Thai 5L value


set. Thus generalizability of these results should be made with cautions. This study

found that among comparable health states between 3L and 5L, all preference scores

generated from the 5L value set was higher than those of the 3L value set resulting in

higher QALY. Thus ICER/QALY calculated using the Thai 5L value set yielded more

cost-effective than the Thai 3L value set. Nevertheless in terms of policy decision

making, only ICERs may not enough to decide which intervention is worth as ICERs

contained some degree of uncertainty from costs and effect variables (110). The

CEAC could help policy maker to better decide as it shows the information on

uncertainty of ICERs estimated. The CEAC from this study showed that the

uncertainty in the results was lower when the utilities obtained from the 5L value set

were employed. Thus using the Thai 5L value set in economic modelling could

produce more certainty of cost-effective evidence and could support better policy

decision making for Thailand context. Further studies comparing economic evaluation

results using preference score from 3L and 5L in different models, diseases, and

population are also needed.

Juntana Pattanaphesaj Conclusions / 114

CHAPTER VI

CONCLUSIONS

The preference score of the 5L for Thai population was estimated using the

lead-time TTO method. The random effect model with only main effect was selected.

The use of country-specific value sets is recommended since the evidence suggested

that the preference score of the Thai 5L value sets was significant different from the

interim scoring generated by the EuroQol group. The DCE valuation generated

inconsistent coefficient in the regression model, indicating the need to further examine

the cause of inconsistency and how to deal with these problems.

In term of measurement properties, this study suggests that the 5L was

better than the 3L in terms of greater distribution, less ceiling effect, more

informativity, more discriminatory power, more reliable for index score, and more

patient preferences. The 5L also showed reasonable convergent validity. In terms of

economic evaluation, the evidences indicated that using population-based 5L value set

in economic model yielded better value for money than population-based 3L value set.

In addition, we found that using population-based 5L value set produced less

uncertainty in cost-effective information compared to the Thai 3L value set.

Thus, the 5L should be recommended as a preferred health-related quality

of life questionnaire in Thailand.


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APPENDICES

Juntana Pattanaphesaj Appendices / 126

APPENDIX A

CERTIFICATE OF ETHICAL CONSIDERATION


APPENDIX B

THE EXAMPLE OF EQ-VT SCREEN

1. On-screen introduction

2. TTO wheelchair example, Life A = 10 years

มปญหาในการเดนเลกนอย มปญหาในการอาบน า หรอใสเสอผาดวยตนเองเลกนอย มปญหาในการท ากจกรรมทท าเปนประจ าปานกลาง มอาการเจบปวดหรออาการไมสบายตวอยางมาก ไมรสกวตกกงวลหรอซมเศรา


3. TTO wheelchair example, Life A = 5 years

4. Lead time TTO example, Life A = 10 years, state worse than dead

มปญหาในการเดนอยางมาก อาบน า หรอใสเสอผาดวยตนเองไมได ไมมปญหาในการท ากจกรรมทท าเปนประจ า มอาการเจบปวดหรออาการไมสบายตวอยางมาก รสกวตกกงวลหรอซมเศราอยางมากทสด

มปญหาในการเดนเลกนอย มปญหาในการอาบน า หรอใสเสอผาดวยตนเองเลกนอย มปญหาในการท ากจกรรมทท าเปนประจ าปานกลาง มอาการเจบปวดหรออาการไมสบายตวอยางมาก ไมรสกวตกกงวลหรอซมเศรา


5. On-screen DCE introduction

มปญหาในการเดนอยางมาก

อาบน า หรอใสเสอผาดวยตนเองไมได

ไมมปญหาในการท ากจกรรมทท าเปนประจ า

มอาการเจบปวดหรออาการไมสบายตวอยางมาก

รสกวตกกงวลหรอซมเศราอยางมากทสด

มปญหาในการเดนเลกนอย

มปญหาในการอาบน า หรอใสเสอผาดวยตนเองเลกนอย

มปญหาในการท ากจกรรมทท าเปนประจ าปานกลาง

มอาการเจบปวดหรออาการไมสบายตวอยางมาก

ไมรสกวตกกงวลหรอซมเศรา

อะไรดกวากน ชวตแบบ A หรอชวตแบบ B

A B


APPENDIX C

EQ-5D-3L THAI VERSION

กรณาท าเครองหมาย x ลงในชองสเหลยมของค าถามแตละขอทตรงกบภาวะสขภาพของทานในวนนมากทสด การเคลอนไหว ขาพเจาไมมปญหาในการเดน ขาพเจามปญหาในการเดนบาง ขาพเจาไมสามารถไปไหนได และจ าเปนตองนอนอยบนเตยง การดแลตนเอง ขาพเจาไมมปญหาในการดแลตนเอง ขาพเจามปญหาในการอาบน าหรอแตงตวบาง ขาพเจาไมสามารถอาบน าหรอแตงตวดวยตนเองได กจกรรมทท าเปนประจ า (เชน การท างาน การเรยนหนงสอ การท างานบาน การท ากจกรรมในครอบครว หรอการท ากจกรรมยามวาง) ขาพเจาไมมปญหาในการท ากจกรรมทท าเปนประจ า ขาพเจามปญหาในการท ากจกรรมทท าเปนประจ าอยบาง ขาพเจาไมสามารถท ากจกรรมทท าเปนประจ าได ความเจบปวด/ไมสขสบาย ขาพเจาไมมอาการเจบปวดหรออาการไมสขสบาย ขาพเจามอาการเจบปวดหรออาการไมสขสบายปานกลาง ขาพเจามอาการเจบปวดหรออาการไมสขสบายมากทสด ความวตกกงวล/ซมเศรา ขาพเจาไมรสกวตกกงวลหรอซมเศรา ขาพเจารสกวตกกงวลหรอซมเศราปานกลาง ขาพเจารสกวตกกงวลหรอซมเศรามากทสด Thailand (Thai) © 2002 EuroQol Group. EQ-5D™ is a trade mark of the EuroQol Group


เพอชวยในการประเมนภาวะสขภาพของทาน, ทางเราไดจดท าสเกลวดระดบสขภาพขน เรมตงแตระดบ 0 ถง 100 โดยท 100 หมายถงภาวะสขภาพทดทสด และ 0 หมายถง ภาวะสขภาพทแยทสด ตามความคดของทาน

กรณาประเมนภาวะสขภาพของทานในวนนวาดหรอไมดเพยงไร โดยการลากเสน จากชองสเหลยมขางลางนไปยงจดบนสเกลวดระดบสขภาพทตรงกบภาวะสขภาพ ของทานในวนน

ภาวะสขภาพของทาน ในวนน

9 0

8 0

7 0

6 0

5 0

4 0

3 0

2 0

1 0

100

ภาวะสขภาพททาน รสกวาแยทสด

0

ภาวะสขภาพททาน รสกวาดทสด

Thailand (Thai) © 2002 EuroQol Group. EQ-5D™ is a trade mark of the EuroQol Group


APPENDIX D

EQ-5D-5L THAI VERSION

แบบสอบถามเรองสขภาพ

ฉบบภาษาไทยส าหรบใชในประเทศไทย

(Thai version for Thailand)



ในแตละหวขอ กรณาท าเครองหมาย ลงในชองสเหลยม เพยงชองเดยว ทตรงกบสขภาพของทานในวนน มากทสด การเคลอนไหว ขาพเจาไมมปญหาในการเดน ขาพเจามปญหาในการเดนเลกนอย ขาพเจามปญหาในการเดนปานกลาง ขาพเจามปญหาในการเดนอยางมาก ขาพเจาเดนไมได การดแลตนเอง ขาพเจาไมมปญหาในการอาบน า หรอใสเสอผาดวยตนเอง ขาพเจามปญหาในการอาบน า หรอใสเสอผาดวยตนเองเลกนอย ขาพเจามปญหาในการอาบน า หรอใสเสอผาดวยตนเองปานกลาง ขาพเจามปญหาในการอาบน า หรอใสเสอผาดวยตนเองอยางมาก ขาพเจาอาบน า หรอใสเสอผาดวยตนเองไมได กจกรรมทท าเปนประจ า (เชน ท ำงำน, เรยนหนงสอ, ท ำงำนบำน, กจกรรมในครอบครว หรอกจกรรมยำมวำง) ขาพเจาไมมปญหาในการท ากจกรรมทท าเปนประจ า ขาพเจามปญหาในการท ากจกรรมทท าเปนประจ าเลกนอย ขาพเจามปญหาในการท ากจกรรมทท าเปนประจ าปานกลาง ขาพเจามปญหาในการท ากจกรรมทท าเปนประจ าอยางมาก ขาพเจาท ากจกรรมทท าเปนประจ าไมได อาการเจบปวด/อาการไมสบายตว ขาพเจาไมมอาการเจบปวดหรออาการไมสบายตว ขาพเจามอาการเจบปวดหรออาการไมสบายตวเลกนอย ขาพเจามอาการเจบปวดหรออาการไมสบายตวปานกลาง ขาพเจามอาการเจบปวดหรออาการไมสบายตวอยางมาก ขาพเจามอาการเจบปวดหรออาการไมสบายตวอยางมากทสด ความวตกกงวล/ความซมเศรา ขาพเจาไมรสกวตกกงวลหรอซมเศรา ขาพเจารสกวตกกงวลหรอซมเศราเลกนอย ขาพเจารสกวตกกงวลหรอซมเศราปานกลาง ขาพเจารสกวตกกงวลหรอซมเศราอยางมาก ขาพเจารสกวตกกงวลหรอซมเศราอยางมากทสด



เราอยากทราบวาสขภาพของทานเปนอยางไรในวนน

สเกลวดสขภาพนมตวเลขตงแต 0 ถง 100

100 หมายถง สขภาพดทสด ตามความคดของทาน

0 หมายถง สขภาพแยทสด ตามความคดของทาน

ท าเครองหมาย X บนสเกลเพอระบวาสขภาพของทานเปนอยางไรในวนน

ตอนน กรณาใสตวเลขทคณไดท าเครองหมายไวบนสเกลในชองสเหลยมดานลางน

สขภาพของทานในวนน =

Thailand (Thai) ©2012 EuroQol Group. EQ-5D™ is a trade mark of the EuroQol Group

10

0

20

30

40

50

60

80

70

90

100

5

15

25

35

45

55

75

65

85

95

สขภาพดทสด

ตามความคดของทาน

สขภาพแยทสด



APPENDIX E

BACKGROUND QUESTIONS

1. Have you experienced serious illness?

In your self Yes No

In your family Yes No

In caring for others Yes No

2. How old are you? years

3. Are you male or female? Male Female

4. What is your marital status?

Single Married Widowed Divorced/Separated

5. How many children do you have?

6. What is the highest level of education that you have completed?

Unlettered Primary school

High school Diploma

Bachelor’s degree Master’s degree or higher

7. What is your main occupation?

Agriculture/fishery Business owner

Unskilled labor Employee

Government/state enterprise officer Housewife

Student Retired

Looking for a job Unable to work due to sickness

Other.......................................................

8. How much is your household income per month? Baht


APPENDIX F

TTO HEALTH STATES INCLUDED IN THE EQ-VT

Block State # State Block State # State Block State # State 1 1 11221 5 33 43315 9 65 11414 2 11235 34 54153 66 25331 3 54231 35 52431 67 25222 4 51451 36 24443 68 21444 5 34515 37 14113 69 31514 6 35245 38 31524 70 53243 7 12514 39 15151 71 53244 8 45144 40 21315 72 35143 82 12111 85 11112 84 11121 86 55555 86 55555 86 55555

2 9 12543 6 41 12112 10 73 11122 10 12121 42 11212 74 52335 11 43542 43 44553 75 35311 12 34155 44 21345 76 43555 13 52215 45 34244 77 24445 14 45133 46 23152 78 13224 15 32443 47 43514 79 34232 16 23514 48 55424 80 42321 83 11211 81 21111 82 12111 86 55555 86 55555 86 55555

3 17 45233 7 49 13122 18 55233 50 24553 19 31525 51 51152 20 52455 52 11425 21 12244 53 22434 22 13313 54 42115 23 25122 55 35332 24 11421 56 45413 81 21111 83 11211 86 55555 86 55555

4 25 21112 8 57 33253 26 14554 58 23242 27 12513 59 24342 28 44345 60 32314 29 12344 61 12334 30 53221 62 21334 31 54342 63 55225 32 44125 64 53412 84 11121 85 11112 86 55555 86 55555


APPENDIX G

TTO FEEDBACK QUESTIONS

Please tell us what you thought about the questions you just answered, where you were

comparing two different ‘lives’

Agr

ee

Dis

agre

e

It was easy to understand the questions I was

asked.

I found it easy to tell the difference between the

lives I was asked to think about.

I found it difficult to decide on the exact points

where Life A and Life B were about the same.


APPENDIX H

DCE PAIRS INCLUDED IN THE EQ-VT

Block Option 1 Option 2 Block Option 1 Option 2 Block Option 1 Option 2 1 35554 55211 6 13432 13245 11 15244 44241 43141 25554 24314 43222 44151 53242 31135 11444 51354 41335 22413 22331 25515 22251 43244 25522 41424 35533 42441 21415 12253 12551 42452 23144 22411 43133 23513 52254 53422 42525 33225 53314 54121 44322 23122 12415

2 52132 21534 7 23551 43135 12 43534 32125 31331 35124 51255 31343 24155 32534 42255 55524 11352 31413 22433 12443 23235 11141 25212 32443 52422 55254 34412 54253 43412 13342 55244 53531 35312 14422 54424 15321 22222 25514 13553 31234 34134 45325 12111 21121

3 51311 32154 8 14552 55325 13 44134 22352 34355 43342 51114 41253 42243 35433 14333 24424 25235 13413 22512 55313 22453 13442 25145 52244 15534 43454 41552 22422 45533 14444 55153 22521 45115 54225 51552 35513 21235 12243 12112 22211 21111 12121 12521 41115

4 44115 21455 9 25312 41532 14 11214 45312 23443 25113 41315 15121 25342 51152 31451 45431 44351 24415 34442 15214 45552 32413 24145 32253 21114 52432 25332 51544 51424 35525 35252 32254 41114 24142 23552 32244 42512 23544 11212 22112 13222 31131 54344 15411

5 12145 15344 10 35321 53215 15 55335 53442 33424 41542 24453 41331 45542 42133 35521 43355 21423 13114 12151 35543 44323 21525 51331 22421 43245 34324 52155 45231 35235 42325 13515 11324 33443 54133 22544 35452 41312 24253 11211 22111 51131 35353 11121 21211


Block Option 1 Option 2 Block Option 1 Option 2 Block Option 1 Option 2 16 23442 25414 21 13111 11215 26 44521 41153 52544 34222 13251 53313 54455 55234 52211 11325 44234 33441 44231 25533 33224 42113 21522 25324 15555 53455 51123 43451 45515 34433 22343 34513 15241 12352 43525 23444 11112 12221 14344 52454 42153 53151 21112 12211

17 52523 54142 22 22341 45145 27 14533 21542 23451 34354 32334 22254 23134 14314 53551 21224 41545 33531 53431 52255 53125 31415 55235 22533 51522 45244 15113 14434 15424 33322 14224 32322 13334 45441 32241 51525 44145 45432 11122 23111 32211 14211 11221 22122

18 33223 21232 23 11512 22241 28 42323 55223 31521 43152 34345 51325 41325 13445 44123 51232 42122 31325 34333 33142 14455 15514 45531 14334 23231 25323 25545 35225 51214 45153 31444 11353 33111 32545 15351 14312 15335 43532 41431 24212 21335 44551 35431 51323

19 35231 53554 24 32442 54441 42421 54255 11545 14113 54423 32314 52223 54132 23233 12411 11234 21532 22123 11155 35322 41535 21445 55141 13131 23113 54454 24511 55534 33355

20 35211 42551 25 33432 15551 34132 24445 14122 54231 24523 45125 51324 34543 52111 11431 33243 11115 21354 41321 34234 13533 54555 35535 23531 53133 11445 32115 53543 41215


APPENDIX I

DCE FEEDBACK QUESTIONS

Please tell us what you thought about the questions you just answered, where you were

comparing two different states of health.

Agr

ee

Dis

agre

e

It was easy to understand the questions I was

asked.

I found it easy to tell the difference between the

health states I was asked to think about.

I found it difficult to decide on my answers to the

questions.


APPENDIX J

COUNTRY-SPECIFIC QUESTIONS

แบบสอบถามเฉพาะประเทศ – paper and pencil version วนทสมภาษณ……………………………………ชอพนกงานสมภาษณ……………………………………… Identifier ………………………………..………อาย............ป เพศ 1. ชาย 2. หญง ทานเหนดวยกบขอความนมากนอยเพยงใด ใหท าเครองหมายวงกลมทตวเลข เพยง 1 ค าตอบ 1. ทกสงทเกดขนกบฉนในชวตนเปนผลมาจากการกระท าของฉนเมอชาตกอนๆ

1 ไมเหนดวย มากทสด

2 ไมเหนดวย

3 เฉย ๆ /

ไมมความคดเหน

4 เหนดวย

5 เหนดวยมากทสด

2. ฉนมคนทจะชวยดแลฉนในยามทเจบปวย 1

ไมเหนดวย มากทสด


3 เฉย ๆ /




3. ไมวาฉนจะเจบปวยรนแรงเพยงใดฉนจะพยายามมชวตอยตอไปใหนานทสดเพอท าบางสงบางอยาง 1



3 เฉย ๆ /




4. การจบชวตเพอหนปญหาเปนสงทผดมากตามความเชอของฉน

1 ไมเหนดวย มากทสด


3 เฉย ๆ /




5. ฉนใชหลกค าสอนทางศาสนาในการเผชญกบปญหาในชวตของฉน 1



3 เฉย ๆ /





APPENDIX K

QUALITATIVE QUESTIONS

ค าถามเชงคณภาพ วนทสมภาษณ……………………………………ชอพนกงานสมภาษณ……………………………………… Identifier ………………………………..………อาย............ป เพศ 1. ชาย 2. หญง

เกณฑการคดเลอกผตอบแบบสอบถาม 1. มลกษณะเฉพาะในการตอบ TTO

1.1 เลอก “ชวตแบบ A & B เหมอนกน” ในจดทชวตแบบ A สนมาก (0-4 ป) เปนสวนใหญ 1.2 เลอกทจะมชวตอยนานๆ (เลอกชวตแบบ B เปนสวนใหญ) แมวาชวตแบบ B จะมอาการรนแรง (มสถานะรนแรงมากทสด/ท าไมได 2 ขอขนไป)

1.3 เลอกทจะเสยชวตเปนสวนใหญ แมชวตแบบ B จะมสถานะสขภาพทไมรนแรง 2. มกจะตดสนใจดวยมตสขภาพเพยง 1-2 มต (เชน การเคลอนไหว การดแลตนเอง) 3. ........................................................................................................................................

1. เหตผลทเลอกค าตอบเชนนน

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........................................................................................................................................................................................ ........................................................................................................................................................................................

2. สถานะสขภาพทง 5 ขอน ขอใดททานคดวาแยทสด และขอใดททานรบไดมากทสด โดยใหท าเครองหมายถก () ลงในชองสเหลยมทตรงกบความคดเหนของทานมากทสด

แยทสด สถานะสขภาพ รบไดมากทสด

1. เดนไมได

2. อาบน า หรอใสเสอผาดวยตนเองไมได

3. ท ากจกรรมทท าเปนประจ าไมได

4. เจบปวดหรออาการไมสบายตวอยางมากทสด

5. วตกกงวลหรอซมเศราอยางมากทสด

กจกรรมทท าเปนประจ า เชน ท างาน, เรยนหนงสอ, ท างานบาน, กจกรรมในครอบครว หรอกจกรรมยามวาง


3. ในเหตการณสมมตน ถาหากมชวตใหทานเลอก 3 แบบ ทานจะเลอกแบบใด เพราะอะไร หากทานมความเจบปวยรนแรงมาก เชน เปนมะเรงระยะสดทาย ซงเจบปวดทรมาน และก าลงจะตองเสยชวตในอกไมชา

รกษาดวยยา โดยไมเสยคาใชจาย ซงชวยใหมชวตตอไปไดอก 5 ป แตยงมอาการเจบปวด ทรมานเหมอนเดม

รกษาดวยยา โดยไมเสยคาใชจาย ซงชวยใหมชวตตอไปไดอก 6 เดอน โดยมสขภาพด ไมมอาการเจบปวด ทรมาน

ยอมเสยชวต โดยไมรกษา

เหตผลทเลอกค าตอบเชนนน

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ขอขอบคณทกทานทใหความรวมมอในการตอบแบบสอบถาม


APPENDIX L

QUESTIONNAIRE FOR TESTING MEASUREMENT PROPERTY

โครงการประเมนเทคโนโลยและนโยบายดานสขภาพ

(Health Intervention and Technology Assessment Program)

โครงการวจย “การวดอรรถประโยชนของผปวยเบาหวานชนดท 1 และชนดท 2 ทพงอนซลน” และ การเปรยบเทยบคณสมบตทางจตวทยาของแบบสอบถาม EQ-5D-3L และ EQ-5D-5L ฉบบภาษาไทย”

สวนประกอบของแบบสมภาษณ

สวนท 1 ขอมลทวไปของผถกสมภาษณ

สวนท 2 แบบสอบถามเรองสขภาพ EQ-5D และความคดเหนตอแบบสอบถาม

สวนท 3 แบบส ารวจสขภาพ SF-36 ฉบบภาษาไทย

แบบสอบถามนมจดประสงคเพอ

1. ประเมนคณภาพชวตผปวยเบาหวานชนดท 1 และชนดท 2 ทพงอนซลน

2. ทดสอบคณสมบตของแบบสอบถาม EQ-5D-5L ฉบบภาษาไทย

ขอมลนไมมการระบชอผถกสมภาษณ และขอมลจะถกเกบไวเปนความลบ

ส าหรบใชในงานวจยนเทานน เพอประโยชนแกการพฒนาระบบบรการสขภาพในอนาคต


สวนท 1 ขอมลทวไปของผถกสมภาษณ หมายเลขแบบสอบถาม

ชอ-นามสกล ผสมภาษณ ................................................................. เรมสมภาษณเวลา ............ : ............

วน เดอน ป (พ.ศ.) ทสมภาษณ //

ผสมภาษณอธบายวตถประสงคของการศกษาวจยแกผถกสมภาษณ

สวนท 1: ขอมลทวไปของผถกสมภาษณ Variable code

1. เพศ

1. ชาย 2. หญง

Sex

2. อาย ............... ปเตม Age

3. สถานภาพ

1. โสด 2. ค 3. หมาย 4. หยา/แยกกนอย

Married

4. ระดบการศกษา

1. ไมไดเรยนหนงสอ 2. ประถมศกษา

3. มธยมศกษา/ปวช. 4. อนปรญญา/ปวส.หรอเทยบเทา

5. ปรญญาตรหรอเทยบเทา 6. สงกวาปรญญาตร

Education

5. อาชพหลก

1. เกษตรกรรม/ประมง 2. คาขาย/เจาของกจการ

3. ผใชแรงงาน/รบจางทวไป 4. พนกงานบรษทเอกชน

5. ขาราชการ/รฐวสาหกจ 6. พอบาน/แมบาน

7. นกเรยน/นกศกษา 8. เกษยณ

9. อยระหวางหางาน 10. ไมสามารถท างานไดเพราะปวย

Occupation

6. รายไดเฉลยของครอบครวตอเดอน....................................................บาท Income


7. สทธการรกษา

1. ประกนสขภาพถวนหนา 2. ประกนสงคม

3. ขาราชการ/รฐวสาหกจ 4. ช าระเอง/ไมมสทธใดๆ

Health_Insure

8. ทานปวยเปนเบาหวานชนด

1. ชนดท 1 2. ชนดท 2

Type

9. ทานปวยเปนเบาหวานมานาน_____ป Duration

10. น าหนก______________กโลกรม Weight

11. สวนสง_________เซนตเมตร height

เสรจเวลา ............ : ........... Time_1


สวนท 2 แบบสอบถามเรองสขภาพ EQ-5D กรณาท าเครองหมาย ลงในชองสเหลยมของค าถามแตละขอทตรงกบภาวะสขภาพของทานในวนนมากทสด ในแตละขอ ทานจะตองตอบ 2 ครง โดยดานซายมค าตอบใหเลอก 5 ระดบ ดานขวามค าตอบใหเลอก 3 ระดบ เรมเวลา ...........:............

12. การเคลอนไหว ขาพเจาไมมปญหาในการเดน ขาพเจามปญหาในการเดนเลกนอย ขาพเจามปญหาในการเดนปานกลาง ขาพเจามปญหาในการเดนอยางมาก ขาพเจาเดนไมได

ขาพเจาไมมปญหาในการเดน ขาพเจามปญหาในการเดนบาง ขาพเจาไมสามารถไปไหนได และจ าเปนตองนอนอยบนเตยง

13. การดแลตนเอง ขาพเจาไมมปญหาในการอาบน าหรอใสเสอผาดวยตนเอง ขาพเจามปญหาในการอาบน า หรอใสเสอผาดวยตนเองเลกนอย ขาพเจามปญหาในการอาบน า หรอใสเสอผาดวยตนเองปานกลาง ขาพเจามปญหาในการอาบน าหรอใสเสอผาดวยตนเองอยางมาก ขาพเจาอาบน าหรอใสเสอผาดวยตนเองไมได

ขาพเจาไมมปญหาในการดแลตนเอง ขาพเจามปญหาในการอาบน าหรอแตงตวบาง ขาพเจาไมสามารถอาบน าหรอแตงตวดวยตนเองได

14. กจกรรมทท าเปนประจ า(เชน ท ำงำน, เรยนหนงสอ, ท ำงำนบำน,กจกรรมในครอบครว หรอกจกรรมยำมวำง) ขาพเจาไมมปญหาในการท ากจกรรมทท าเปนประจ า ขาพเจามปญหาในการท ากจกรรมทท าเปนประจ าเลกนอย ขาพเจามปญหาในการท ากจกรรมทท าเปนประจ าปานกลาง ขาพเจามปญหาในการท ากจกรรมทท าเปนประจ าอยางมาก ขาพเจาท ากจกรรมทท าเปนประจ าไมได

ขาพเจาไมมปญหาในการท ากจกรรมทท าเปนประจ า ขาพเจามปญหาในการท ากจกรรมทท าเปนประจ าอยบาง ขาพเจาไมสามารถท ากจกรรมทท าเปนประจ าได

15. อาการเจบปวด/อาการไมสบายตว ขาพเจาไมมอาการเจบปวดหรออาการไมสบายตว ขาพเจามอาการเจบปวดหรออาการไมสบายตวเลกนอย ขาพเจามอาการเจบปวดหรออาการไมสบายตวปานกลาง ขาพเจามอาการเจบปวดหรออาการไมสบายตวอยางมาก ขาพเจามอาการเจบปวดหรออาการไมสบายตวอยางมากทสด

ขาพเจาไมมอาการเจบปวดหรออาการไมสขสบาย ขาพเจามอาการเจบปวดหรออาการไมสขสบายปานกลาง ขาพเจามอาการเจบปวดหรออาการไมสขสบายมากทสด

16. ความวตกกงวล/ความซมเศรา ขาพเจาไมรสกวตกกงวลหรอซมเศรา ขาพเจารสกวตกกงวลหรอซมเศราเลกนอย ขาพเจารสกวตกกงวลหรอซมเศราปานกลาง ขาพเจารสกวตกกงวลหรอซมเศราอยางมาก ขาพเจารสกวตกกงวลหรอซมเศราอยางมากทสด

ขาพเจาไมรสกวตกกงวลหรอซมเศรา ขาพเจารสกวตกกงวลหรอซมเศราปานกลาง ขาพเจารสกวตกกงวลหรอซมเศรามากทสด


เราอยากทราบวาสขภาพของทานเปนอยางไรในวนน

สเกลวดสขภาพนมตวเลขตงแต 0 ถง 100

100 หมายถง สขภาพดทสด ตามความคดของทาน

0 หมายถง สขภาพแยทสด ตามความคดของทาน

ท าเครองหมาย X บนสเกลเพอระบวาสขภาพของทานเปนอยางไรในวนน

ตอนน กรณาใสตวเลขทคณไดท าเครองหมายไวบนสเกลในชองสเหลยม

ดานลางน

สขภาพของทานในวนน =

10

0

20

30

40

50

60

80

70

90

100

5

15

25

35

45

55

75

65

85

95

สขภาพดทสด


สขภาพแยทสด




17. จากแบบสอบถามคณภาพชวตดานสขภาพแบบทมค าตอบใหเลอก (ขอ 12-16) ลกษณะค าตอบแบบใดทตอบงายทสด ตามความคดเหนของทาน ค าตอบ 3 ระดบ ค าตอบ 5 ระดบ ไมตางกน

18. จากแบบสอบถามคณภาพชวตดานสขภาพแบบทมค าตอบใหเลอก (ขอ 12-16) ลกษณะค าตอบแบบใดท

สะทอนสขภาพของทานไดดทสด ตามความคดเหนของทาน

ค าตอบ 3 ระดบ ค าตอบ 5 ระดบ ไมตางกน


APPENDIX M

SF-36V2 THAI VERSION


APPENDIX N

THAI PREFERENCE SCORE FOR EQ-5D-5L HEALTH STATES

Profile utility Profile utility Profile utility 11111 1.000 11243 0.627 11425 0.547

11112 0.968 11244 0.521 11431 0.767

11113 0.903 11245 0.475 11432 0.735

11114 0.798 11251 0.691 11433 0.671

11115 0.751 11252 0.659 11434 0.565

11121 0.960 11253 0.594 11435 0.519

11122 0.928 11254 0.488 11441 0.602

11123 0.864 11255 0.442 11442 0.570

11124 0.758 11311 0.925 11443 0.505

11125 0.712 11312 0.893 11444 0.400

11131 0.932 11313 0.829 11445 0.353

11132 0.900 11314 0.723 11451 0.569

11133 0.835 11315 0.677 11452 0.537

11134 0.730 11321 0.886 11453 0.473

11135 0.683 11322 0.854 11454 0.367

11141 0.767 11323 0.789 11455 0.320

11142 0.734 11324 0.683 11511 0.793

11143 0.670 11325 0.637 11512 0.761

11144 0.564 11331 0.857 11513 0.696

11145 0.518 11332 0.825 11514 0.590

11151 0.734 11333 0.761 11515 0.544

11152 0.702 11334 0.655 11521 0.753

11153 0.637 11335 0.609 11522 0.721

11154 0.531 11341 0.692 11523 0.656

11155 0.485 11342 0.660 11524 0.551

11211 0.957 11343 0.595 11525 0.504

11212 0.925 11344 0.490 11531 0.725

11213 0.860 11345 0.443 11532 0.693

11214 0.755 11351 0.659 11533 0.628

11215 0.708 11352 0.627 11534 0.522

11221 0.917 11353 0.563 11535 0.476

11222 0.885 11354 0.457 11541 0.559

11223 0.821 11355 0.410 11542 0.527

11224 0.715 11411 0.835 11543 0.463

11225 0.669 11412 0.803 11544 0.357

11231 0.889 11413 0.739 11545 0.310

11232 0.857 11414 0.633 11551 0.526

11233 0.792 11415 0.587 11552 0.494

11234 0.687 11421 0.796 11553 0.430

11235 0.640 11422 0.764 11554 0.324

11241 0.724 11423 0.699 11555 0.278

11242 0.691 11424 0.593



12112 0.935 12244 0.488 12431 0.734

12113 0.870 12245 0.442 12432 0.702

12114 0.765 12251 0.658 12433 0.638

12115 0.718 12252 0.626 12434 0.532

12121 0.927 12253 0.561 12435 0.485

12122 0.895 12254 0.455 12441 0.569

12123 0.831 12255 0.409 12442 0.537

12124 0.725 12311 0.892 12443 0.472

12125 0.678 12312 0.860 12444 0.366

12131 0.899 12313 0.796 12445 0.320

12132 0.867 12314 0.690 12451 0.536

12133 0.802 12315 0.643 12452 0.504

12134 0.696 12321 0.853 12453 0.439

12135 0.650 12322 0.820 12454 0.334

12141 0.733 12323 0.756 12455 0.287

12142 0.701 12324 0.650 12511 0.760

12143 0.637 12325 0.604 12512 0.727

12144 0.531 12331 0.824 12513 0.663

12145 0.485 12332 0.792 12514 0.557

12151 0.701 12333 0.728 12515 0.511

12152 0.669 12334 0.622 12521 0.720

12153 0.604 12335 0.575 12522 0.688

12154 0.498 12341 0.659 12523 0.623

12155 0.452 12342 0.627 12524 0.518

12211 0.924 12343 0.562 12525 0.471

12212 0.892 12344 0.456 12531 0.691

12213 0.827 12345 0.410 12532 0.659

12214 0.722 12351 0.626 12533 0.595

12215 0.675 12352 0.594 12534 0.489

12221 0.884 12353 0.529 12535 0.443

12222 0.852 12354 0.424 12541 0.526

12223 0.788 12355 0.377 12542 0.494

12224 0.682 12411 0.802 12543 0.430

12225 0.635 12412 0.770 12544 0.324

12231 0.856 12413 0.706 12545 0.277

12232 0.824 12414 0.600 12551 0.493

12233 0.759 12415 0.553 12552 0.461

12234 0.654 12421 0.763 12553 0.397

12235 0.607 12422 0.731 12554 0.291

12241 0.690 12423 0.666 12555 0.244

12242 0.658 12424 0.560



13112 0.860 13244 0.413 13431 0.659

13113 0.795 13245 0.367 13432 0.627

13114 0.690 13251 0.583 13433 0.563

13115 0.643 13252 0.551 13434 0.457

13121 0.852 13253 0.486 13435 0.411

13122 0.820 13254 0.380 13441 0.494

13123 0.756 13255 0.334 13442 0.462

13124 0.650 13311 0.817 13443 0.397

13125 0.604 13312 0.785 13444 0.292

13131 0.824 13313 0.721 13445 0.245

13132 0.792 13314 0.615 13451 0.461

13133 0.727 13315 0.569 13452 0.429

13134 0.622 13321 0.778 13453 0.365

13135 0.575 13322 0.746 13454 0.259

13141 0.659 13323 0.681 13455 0.212

13142 0.626 13324 0.575 13511 0.685

13143 0.562 13325 0.529 13512 0.653

13144 0.456 13331 0.749 13513 0.588

13145 0.410 13332 0.717 13514 0.482

13151 0.626 13333 0.653 13515 0.436

13152 0.594 13334 0.547 13521 0.645

13153 0.529 13335 0.501 13522 0.613

13154 0.423 13341 0.584 13523 0.549

13155 0.377 13342 0.552 13524 0.443

13211 0.849 13343 0.487 13525 0.396

13212 0.817 13344 0.382 13531 0.617

13213 0.752 13345 0.335 13532 0.585

13214 0.647 13351 0.551 13533 0.520

13215 0.600 13352 0.519 13534 0.414

13221 0.809 13353 0.455 13535 0.368

13222 0.777 13354 0.349 13541 0.451

13223 0.713 13355 0.302 13542 0.419

13224 0.607 13411 0.727 13543 0.355

13225 0.561 13412 0.695 13544 0.249

13231 0.781 13413 0.631 13545 0.202

13232 0.749 13414 0.525 13551 0.418

13233 0.684 13415 0.479 13552 0.386

13234 0.579 13421 0.688 13553 0.322

13235 0.532 13422 0.656 13554 0.216

13241 0.616 13423 0.591 13555 0.170

13242 0.584 13424 0.485



14112 0.743 14244 0.297 14431 0.543

14113 0.679 14245 0.250 14432 0.510

14114 0.573 14251 0.466 14433 0.446

14115 0.526 14252 0.434 14434 0.340

14121 0.736 14253 0.369 14435 0.294

14122 0.704 14254 0.264 14441 0.377

14123 0.639 14255 0.217 14442 0.345

14124 0.533 14311 0.701 14443 0.281

14125 0.487 14312 0.669 14444 0.175

14131 0.707 14313 0.604 14445 0.128

14132 0.675 14314 0.498 14451 0.344

14133 0.611 14315 0.452 14452 0.312

14134 0.505 14321 0.661 14453 0.248

14135 0.458 14322 0.629 14454 0.142

14141 0.542 14323 0.564 14455 0.096

14142 0.510 14324 0.459 14511 0.568

14143 0.445 14325 0.412 14512 0.536

14144 0.339 14331 0.633 14513 0.471

14145 0.293 14332 0.600 14514 0.366

14151 0.509 14333 0.536 14515 0.319

14152 0.477 14334 0.430 14521 0.528

14153 0.412 14335 0.384 14522 0.496

14154 0.307 14341 0.467 14523 0.432

14155 0.260 14342 0.435 14524 0.326

14211 0.732 14343 0.371 14525 0.279

14212 0.700 14344 0.265 14531 0.500

14213 0.636 14345 0.218 14532 0.468

14214 0.530 14351 0.434 14533 0.403

14215 0.483 14352 0.402 14534 0.298

14221 0.693 14353 0.338 14535 0.251

14222 0.661 14354 0.232 14541 0.334

14223 0.596 14355 0.186 14542 0.302

14224 0.490 14411 0.611 14543 0.238

14225 0.444 14412 0.579 14544 0.132

14231 0.664 14413 0.514 14545 0.086

14232 0.632 14414 0.408 14551 0.302

14233 0.568 14415 0.362 14552 0.270

14234 0.462 14421 0.571 14553 0.205

14235 0.415 14422 0.539 14554 0.099

14241 0.499 14423 0.474 14555 0.053

14242 0.467 14424 0.369



15112 0.714 15244 0.268 15431 0.514

15113 0.650 15245 0.221 15432 0.482

15114 0.544 15251 0.437 15433 0.417

15115 0.498 15252 0.405 15434 0.311

15121 0.707 15253 0.341 15435 0.265

15122 0.675 15254 0.235 15441 0.348

15123 0.610 15255 0.188 15442 0.316

15124 0.504 15311 0.672 15443 0.252

15125 0.458 15312 0.640 15444 0.146

15131 0.678 15313 0.575 15445 0.100

15132 0.646 15314 0.469 15451 0.316

15133 0.582 15315 0.423 15452 0.283

15134 0.476 15321 0.632 15453 0.219

15135 0.430 15322 0.600 15454 0.113

15141 0.513 15323 0.536 15455 0.067

15142 0.481 15324 0.430 15511 0.539

15143 0.416 15325 0.383 15512 0.507

15144 0.311 15331 0.604 15513 0.443

15145 0.264 15332 0.572 15514 0.337

15151 0.480 15333 0.507 15515 0.290

15152 0.448 15334 0.401 15521 0.499

15153 0.384 15335 0.355 15522 0.467

15154 0.278 15341 0.438 15523 0.403

15155 0.231 15342 0.406 15524 0.297

15211 0.703 15343 0.342 15525 0.251

15212 0.671 15344 0.236 15531 0.471

15213 0.607 15345 0.190 15532 0.439

15214 0.501 15351 0.406 15533 0.374

15215 0.455 15352 0.373 15534 0.269

15221 0.664 15353 0.309 15535 0.222

15222 0.632 15354 0.203 15541 0.306

15223 0.567 15355 0.157 15542 0.274

15224 0.461 15411 0.582 15543 0.209

15225 0.415 15412 0.550 15544 0.103

15231 0.635 15413 0.485 15545 0.057

15232 0.603 15414 0.379 15551 0.273

15233 0.539 15415 0.333 15552 0.241

15234 0.433 15421 0.542 15553 0.176

15235 0.387 15422 0.510 15554 0.071

15241 0.470 15423 0.446 15555 0.024

15242 0.438 15424 0.340



21112 0.912 21244 0.465 21431 0.711

21113 0.847 21245 0.418 21432 0.679

21114 0.741 21251 0.634 21433 0.614

21115 0.695 21252 0.602 21434 0.509

21121 0.904 21253 0.538 21435 0.462

21122 0.872 21254 0.432 21441 0.546

21123 0.807 21255 0.386 21442 0.514

21124 0.702 21311 0.869 21443 0.449

21125 0.655 21312 0.837 21444 0.343

21131 0.876 21313 0.772 21445 0.297

21132 0.844 21314 0.667 21451 0.513

21133 0.779 21315 0.620 21452 0.481

21134 0.673 21321 0.829 21453 0.416

21135 0.627 21322 0.797 21454 0.311

21141 0.710 21323 0.733 21455 0.264

21142 0.678 21324 0.627 21511 0.736

21143 0.614 21325 0.581 21512 0.704

21144 0.508 21331 0.801 21513 0.640

21145 0.461 21332 0.769 21514 0.534

21151 0.677 21333 0.704 21515 0.488

21152 0.645 21334 0.599 21521 0.697

21153 0.581 21335 0.552 21522 0.665

21154 0.475 21341 0.636 21523 0.600

21155 0.429 21342 0.604 21524 0.494

21211 0.901 21343 0.539 21525 0.448

21212 0.869 21344 0.433 21531 0.668

21213 0.804 21345 0.387 21532 0.636

21214 0.698 21351 0.603 21533 0.572

21215 0.652 21352 0.571 21534 0.466

21221 0.861 21353 0.506 21535 0.420

21222 0.829 21354 0.401 21541 0.503

21223 0.764 21355 0.354 21542 0.471

21224 0.659 21411 0.779 21543 0.406

21225 0.612 21412 0.747 21544 0.301

21231 0.833 21413 0.683 21545 0.254

21232 0.801 21414 0.577 21551 0.470

21233 0.736 21415 0.530 21552 0.438

21234 0.630 21421 0.739 21553 0.374

21235 0.584 21422 0.707 21554 0.268

21241 0.667 21423 0.643 21555 0.221

21242 0.635 21424 0.537



22112 0.878 22244 0.432 22431 0.678

22113 0.814 22245 0.385 22432 0.646

22114 0.708 22251 0.601 22433 0.581

22115 0.662 22252 0.569 22434 0.476

22121 0.871 22253 0.505 22435 0.429

22122 0.839 22254 0.399 22441 0.512

22123 0.774 22255 0.353 22442 0.480

22124 0.669 22311 0.836 22443 0.416

22125 0.622 22312 0.804 22444 0.310

22131 0.842 22313 0.739 22445 0.264

22132 0.810 22314 0.634 22451 0.480

22133 0.746 22315 0.587 22452 0.448

22134 0.640 22321 0.796 22453 0.383

22135 0.594 22322 0.764 22454 0.277

22141 0.677 22323 0.700 22455 0.231

22142 0.645 22324 0.594 22511 0.703

22143 0.581 22325 0.547 22512 0.671

22144 0.475 22331 0.768 22513 0.607

22145 0.428 22332 0.736 22514 0.501

22151 0.644 22333 0.671 22515 0.454

22152 0.612 22334 0.566 22521 0.664

22153 0.548 22335 0.519 22522 0.632

22154 0.442 22341 0.602 22523 0.567

22155 0.395 22342 0.570 22524 0.461

22211 0.868 22343 0.506 22525 0.415

22212 0.835 22344 0.400 22531 0.635

22213 0.771 22345 0.354 22532 0.603

22214 0.665 22351 0.570 22533 0.539

22215 0.619 22352 0.538 22534 0.433

22221 0.828 22353 0.473 22535 0.386

22222 0.796 22354 0.367 22541 0.470

22223 0.731 22355 0.321 22542 0.438

22224 0.626 22411 0.746 22543 0.373

22225 0.579 22412 0.714 22544 0.267

22231 0.800 22413 0.649 22545 0.221

22232 0.767 22414 0.544 22551 0.437

22233 0.703 22415 0.497 22552 0.405

22234 0.597 22421 0.706 22553 0.340

22235 0.551 22422 0.674 22554 0.235

22241 0.634 22423 0.610 22555 0.188

22242 0.602 22424 0.504



23112 0.804 23244 0.357 23431 0.603

23113 0.739 23245 0.310 23432 0.571

23114 0.633 23251 0.527 23433 0.506

23115 0.587 23252 0.494 23434 0.401

23121 0.796 23253 0.430 23435 0.354

23122 0.764 23254 0.324 23441 0.438

23123 0.699 23255 0.278 23442 0.406

23124 0.594 23311 0.761 23443 0.341

23125 0.547 23312 0.729 23444 0.235

23131 0.768 23313 0.665 23445 0.189

23132 0.736 23314 0.559 23451 0.405

23133 0.671 23315 0.512 23452 0.373

23134 0.565 23321 0.721 23453 0.308

23135 0.519 23322 0.689 23454 0.203

23141 0.602 23323 0.625 23455 0.156

23142 0.570 23324 0.519 23511 0.628

23143 0.506 23325 0.473 23512 0.596

23144 0.400 23331 0.693 23513 0.532

23145 0.353 23332 0.661 23514 0.426

23151 0.569 23333 0.596 23515 0.380

23152 0.537 23334 0.491 23521 0.589

23153 0.473 23335 0.444 23522 0.557

23154 0.367 23341 0.528 23523 0.492

23155 0.321 23342 0.496 23524 0.386

23211 0.793 23343 0.431 23525 0.340

23212 0.761 23344 0.325 23531 0.560

23213 0.696 23345 0.279 23532 0.528

23214 0.590 23351 0.495 23533 0.464

23215 0.544 23352 0.463 23534 0.358

23221 0.753 23353 0.398 23535 0.312

23222 0.721 23354 0.293 23541 0.395

23223 0.657 23355 0.246 23542 0.363

23224 0.551 23411 0.671 23543 0.298

23225 0.504 23412 0.639 23544 0.193

23231 0.725 23413 0.575 23545 0.146

23232 0.693 23414 0.469 23551 0.362

23233 0.628 23415 0.422 23552 0.330

23234 0.522 23421 0.631 23553 0.266

23235 0.476 23422 0.599 23554 0.160

23241 0.559 23423 0.535 23555 0.113

23242 0.527 23424 0.429



24112 0.687 24244 0.240 24431 0.486

24113 0.622 24245 0.194 24432 0.454

24114 0.517 24251 0.410 24433 0.390

24115 0.470 24252 0.378 24434 0.284

24121 0.679 24253 0.313 24435 0.237

24122 0.647 24254 0.207 24441 0.321

24123 0.583 24255 0.161 24442 0.289

24124 0.477 24311 0.644 24443 0.224

24125 0.430 24312 0.612 24444 0.119

24131 0.651 24313 0.548 24445 0.072

24132 0.619 24314 0.442 24451 0.288

24133 0.554 24315 0.395 24452 0.256

24134 0.449 24321 0.605 24453 0.191

24135 0.402 24322 0.573 24454 0.086

24141 0.485 24323 0.508 24455 0.039

24142 0.453 24324 0.402 24511 0.512

24143 0.389 24325 0.356 24512 0.480

24144 0.283 24331 0.576 24513 0.415

24145 0.237 24332 0.544 24514 0.309

24151 0.453 24333 0.480 24515 0.263

24152 0.421 24334 0.374 24521 0.472

24153 0.356 24335 0.327 24522 0.440

24154 0.250 24341 0.411 24523 0.375

24155 0.204 24342 0.379 24524 0.270

24211 0.676 24343 0.314 24525 0.223

24212 0.644 24344 0.209 24531 0.444

24213 0.579 24345 0.162 24532 0.411

24214 0.474 24351 0.378 24533 0.347

24215 0.427 24352 0.346 24534 0.241

24221 0.636 24353 0.281 24535 0.195

24222 0.604 24354 0.176 24541 0.278

24223 0.540 24355 0.129 24542 0.246

24224 0.434 24411 0.554 24543 0.182

24225 0.387 24412 0.522 24544 0.076

24231 0.608 24413 0.458 24545 0.029

24232 0.576 24414 0.352 24551 0.245

24233 0.511 24415 0.306 24552 0.213

24234 0.406 24421 0.515 24553 0.149

24235 0.359 24422 0.483 24554 0.043

24241 0.443 24423 0.418 24555 -0.003

24242 0.410 24424 0.312



25112 0.658 25244 0.211 25431 0.457

25113 0.593 25245 0.165 25432 0.425

25114 0.488 25251 0.381 25433 0.361

25115 0.441 25252 0.349 25434 0.255

25121 0.650 25253 0.284 25435 0.209

25122 0.618 25254 0.179 25441 0.292

25123 0.554 25255 0.132 25442 0.260

25124 0.448 25311 0.615 25443 0.195

25125 0.402 25312 0.583 25444 0.090

25131 0.622 25313 0.519 25445 0.043

25132 0.590 25314 0.413 25451 0.259

25133 0.525 25315 0.367 25452 0.227

25134 0.420 25321 0.576 25453 0.163

25135 0.373 25322 0.544 25454 0.057

25141 0.457 25323 0.479 25455 0.010

25142 0.425 25324 0.374 25511 0.483

25143 0.360 25325 0.327 25512 0.451

25144 0.254 25331 0.547 25513 0.386

25145 0.208 25332 0.515 25514 0.280

25151 0.424 25333 0.451 25515 0.234

25152 0.392 25334 0.345 25521 0.443

25153 0.327 25335 0.299 25522 0.411

25154 0.222 25341 0.382 25523 0.347

25155 0.175 25342 0.350 25524 0.241

25211 0.647 25343 0.285 25525 0.194

25212 0.615 25344 0.180 25531 0.415

25213 0.551 25345 0.133 25532 0.383

25214 0.445 25351 0.349 25533 0.318

25215 0.398 25352 0.317 25534 0.212

25221 0.607 25353 0.253 25535 0.166

25222 0.575 25354 0.147 25541 0.249

25223 0.511 25355 0.100 25542 0.217

25224 0.405 25411 0.525 25543 0.153

25225 0.359 25412 0.493 25544 0.047

25231 0.579 25413 0.429 25545 0.001

25232 0.547 25414 0.323 25551 0.217

25233 0.482 25415 0.277 25552 0.184

25234 0.377 25421 0.486 25553 0.120

25235 0.330 25422 0.454 25554 0.014

25241 0.414 25423 0.389 25555 -0.032

25242 0.382 25424 0.284



31112 0.853 31244 0.407 31431 0.653

31113 0.789 31245 0.360 31432 0.621

31114 0.683 31251 0.576 31433 0.556

31115 0.637 31252 0.544 31434 0.451

31121 0.846 31253 0.480 31435 0.404

31122 0.814 31254 0.374 31441 0.487

31123 0.749 31255 0.327 31442 0.455

31124 0.644 31311 0.811 31443 0.391

31125 0.597 31312 0.779 31444 0.285

31131 0.817 31313 0.714 31445 0.239

31132 0.785 31314 0.609 31451 0.455

31133 0.721 31315 0.562 31452 0.423

31134 0.615 31321 0.771 31453 0.358

31135 0.569 31322 0.739 31454 0.252

31141 0.652 31323 0.675 31455 0.206

31142 0.620 31324 0.569 31511 0.678

31143 0.555 31325 0.522 31512 0.646

31144 0.450 31331 0.743 31513 0.582

31145 0.403 31332 0.711 31514 0.476

31151 0.619 31333 0.646 31515 0.429

31152 0.587 31334 0.541 31521 0.639

31153 0.523 31335 0.494 31522 0.606

31154 0.417 31341 0.577 31523 0.542

31155 0.370 31342 0.545 31524 0.436

31211 0.843 31343 0.481 31525 0.390

31212 0.810 31344 0.375 31531 0.610

31213 0.746 31345 0.329 31532 0.578

31214 0.640 31351 0.545 31533 0.514

31215 0.594 31352 0.513 31534 0.408

31221 0.803 31353 0.448 31535 0.361

31222 0.771 31354 0.342 31541 0.445

31223 0.706 31355 0.296 31542 0.413

31224 0.601 31411 0.721 31543 0.348

31225 0.554 31412 0.689 31544 0.242

31231 0.774 31413 0.624 31545 0.196

31232 0.742 31414 0.519 31551 0.412

31233 0.678 31415 0.472 31552 0.380

31234 0.572 31421 0.681 31553 0.315

31235 0.526 31422 0.649 31554 0.210

31241 0.609 31423 0.585 31555 0.163

31242 0.577 31424 0.479



32112 0.820 32244 0.374 32431 0.620

32113 0.756 32245 0.327 32432 0.588

32114 0.650 32251 0.543 32433 0.523

32115 0.604 32252 0.511 32434 0.417

32121 0.813 32253 0.447 32435 0.371

32122 0.781 32254 0.341 32441 0.454

32123 0.716 32255 0.294 32442 0.422

32124 0.610 32311 0.778 32443 0.358

32125 0.564 32312 0.746 32444 0.252

32131 0.784 32313 0.681 32445 0.205

32132 0.752 32314 0.575 32451 0.422

32133 0.688 32315 0.529 32452 0.389

32134 0.582 32321 0.738 32453 0.325

32135 0.535 32322 0.706 32454 0.219

32141 0.619 32323 0.642 32455 0.173

32142 0.587 32324 0.536 32511 0.645

32143 0.522 32325 0.489 32512 0.613

32144 0.417 32331 0.710 32513 0.548

32145 0.370 32332 0.678 32514 0.443

32151 0.586 32333 0.613 32515 0.396

32152 0.554 32334 0.507 32521 0.605

32153 0.490 32335 0.461 32522 0.573

32154 0.384 32341 0.544 32523 0.509

32155 0.337 32342 0.512 32524 0.403

32211 0.809 32343 0.448 32525 0.357

32212 0.777 32344 0.342 32531 0.577

32213 0.713 32345 0.295 32532 0.545

32214 0.607 32351 0.512 32533 0.480

32215 0.561 32352 0.479 32534 0.375

32221 0.770 32353 0.415 32535 0.328

32222 0.738 32354 0.309 32541 0.412

32223 0.673 32355 0.263 32542 0.380

32224 0.567 32411 0.688 32543 0.315

32225 0.521 32412 0.656 32544 0.209

32231 0.741 32413 0.591 32545 0.163

32232 0.709 32414 0.485 32551 0.379

32233 0.645 32415 0.439 32552 0.347

32234 0.539 32421 0.648 32553 0.282

32235 0.493 32422 0.616 32554 0.177

32241 0.576 32423 0.552 32555 0.130

32242 0.544 32424 0.446



33112 0.745 33244 0.299 33431 0.545

33113 0.681 33245 0.252 33432 0.513

33114 0.575 33251 0.468 33433 0.448

33115 0.529 33252 0.436 33434 0.343

33121 0.738 33253 0.372 33435 0.296

33122 0.706 33254 0.266 33441 0.380

33123 0.641 33255 0.220 33442 0.347

33124 0.536 33311 0.703 33443 0.283

33125 0.489 33312 0.671 33444 0.177

33131 0.709 33313 0.606 33445 0.131

33132 0.677 33314 0.501 33451 0.347

33133 0.613 33315 0.454 33452 0.315

33134 0.507 33321 0.663 33453 0.250

33135 0.461 33322 0.631 33454 0.144

33141 0.544 33323 0.567 33455 0.098

33142 0.512 33324 0.461 33511 0.570

33143 0.448 33325 0.414 33512 0.538

33144 0.342 33331 0.635 33513 0.474

33145 0.295 33332 0.603 33514 0.368

33151 0.511 33333 0.538 33515 0.321

33152 0.479 33334 0.433 33521 0.531

33153 0.415 33335 0.386 33522 0.499

33154 0.309 33341 0.469 33523 0.434

33155 0.262 33342 0.437 33524 0.328

33211 0.735 33343 0.373 33525 0.282

33212 0.702 33344 0.267 33531 0.502

33213 0.638 33345 0.221 33532 0.470

33214 0.532 33351 0.437 33533 0.406

33215 0.486 33352 0.405 33534 0.300

33221 0.695 33353 0.340 33535 0.253

33222 0.663 33354 0.234 33541 0.337

33223 0.598 33355 0.188 33542 0.305

33224 0.493 33411 0.613 33543 0.240

33225 0.446 33412 0.581 33544 0.134

33231 0.667 33413 0.516 33545 0.088

33232 0.634 33414 0.411 33551 0.304

33233 0.570 33415 0.364 33552 0.272

33234 0.464 33421 0.573 33553 0.207

33235 0.418 33422 0.541 33554 0.102

33241 0.501 33423 0.477 33555 0.055

33242 0.469 33424 0.371



34112 0.629 34244 0.182 34431 0.428

34113 0.564 34245 0.136 34432 0.396

34114 0.458 34251 0.352 34433 0.332

34115 0.412 34252 0.319 34434 0.226

34121 0.621 34253 0.255 34435 0.179

34122 0.589 34254 0.149 34441 0.263

34123 0.525 34255 0.103 34442 0.231

34124 0.419 34311 0.586 34443 0.166

34125 0.372 34312 0.554 34444 0.060

34131 0.593 34313 0.490 34445 0.014

34132 0.561 34314 0.384 34451 0.230

34133 0.496 34315 0.337 34452 0.198

34134 0.390 34321 0.546 34453 0.133

34135 0.344 34322 0.514 34454 0.028

34141 0.427 34323 0.450 34455 -0.019

34142 0.395 34324 0.344 34511 0.453

34143 0.331 34325 0.298 34512 0.421

34144 0.225 34331 0.518 34513 0.357

34145 0.178 34332 0.486 34514 0.251

34151 0.395 34333 0.421 34515 0.205

34152 0.362 34334 0.316 34521 0.414

34153 0.298 34335 0.269 34522 0.382

34154 0.192 34341 0.353 34523 0.317

34155 0.146 34342 0.321 34524 0.211

34211 0.618 34343 0.256 34525 0.165

34212 0.586 34344 0.150 34531 0.385

34213 0.521 34345 0.104 34532 0.353

34214 0.415 34351 0.320 34533 0.289

34215 0.369 34352 0.288 34534 0.183

34221 0.578 34353 0.223 34535 0.137

34222 0.546 34354 0.118 34541 0.220

34223 0.482 34355 0.071 34542 0.188

34224 0.376 34411 0.496 34543 0.123

34225 0.329 34412 0.464 34544 0.018

34231 0.550 34413 0.400 34545 -0.029

34232 0.518 34414 0.294 34551 0.187

34233 0.453 34415 0.247 34552 0.155

34234 0.347 34421 0.457 34553 0.091

34235 0.301 34422 0.424 34554 -0.015

34241 0.384 34423 0.360 34555 -0.062

34242 0.352 34424 0.254



35112 0.600 35244 0.153 35431 0.399

35113 0.535 35245 0.107 35432 0.367

35114 0.430 35251 0.323 35433 0.303

35115 0.383 35252 0.291 35434 0.197

35121 0.592 35253 0.226 35435 0.150

35122 0.560 35254 0.120 35441 0.234

35123 0.496 35255 0.074 35442 0.202

35124 0.390 35311 0.557 35443 0.137

35125 0.343 35312 0.525 35444 0.032

35131 0.564 35313 0.461 35445 -0.015

35132 0.532 35314 0.355 35451 0.201

35133 0.467 35315 0.308 35452 0.169

35134 0.362 35321 0.518 35453 0.104

35135 0.315 35322 0.486 35454 -0.001

35141 0.398 35323 0.421 35455 -0.048

35142 0.366 35324 0.315 35511 0.425

35143 0.302 35325 0.269 35512 0.393

35144 0.196 35331 0.489 35513 0.328

35145 0.150 35332 0.457 35514 0.222

35151 0.366 35333 0.393 35515 0.176

35152 0.334 35334 0.287 35521 0.385

35153 0.269 35335 0.240 35522 0.353

35154 0.163 35341 0.324 35523 0.288

35155 0.117 35342 0.292 35524 0.183

35211 0.589 35343 0.227 35525 0.136

35212 0.557 35344 0.122 35531 0.357

35213 0.492 35345 0.075 35532 0.324

35214 0.387 35351 0.291 35533 0.260

35215 0.340 35352 0.259 35534 0.154

35221 0.549 35353 0.194 35535 0.108

35222 0.517 35354 0.089 35541 0.191

35223 0.453 35355 0.042 35542 0.159

35224 0.347 35411 0.467 35543 0.095

35225 0.300 35412 0.435 35544 -0.011

35231 0.521 35413 0.371 35545 -0.058

35232 0.489 35414 0.265 35551 0.158

35233 0.424 35415 0.218 35552 0.126

35234 0.319 35421 0.428 35553 0.062

35235 0.272 35422 0.396 35554 -0.044

35241 0.355 35423 0.331 35555 -0.090

35242 0.323 35424 0.225



41112 0.736 41244 0.290 41431 0.536

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Profile utility Profile utility Profile utility 55111 0.439 55243 0.066 55425 -0.014

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55242 0.131 55424 0.033

Juntana Pattanaphesaj Biography / 180

BIOGRAPHY

NAME Miss Juntana Pattanaphesaj

DATE OF BIRTH July 19, 1974

PLACE OF BIRTH Kampangphet, Thailand

INSTITUTIONS ATTENDED Chiang Mai University, 1992-1997:

Bachelor of Science in Pharmacy

Mahidol University, 2005-2007:

Master of Science in Pharmacy

(Pharmacy Administration)

Mahidol University, 2010-2014:

Doctor of Philosophy

(Pharmacy Administration)

RESEARCH GRANTS 1) Burden of Diseases Project, Thailand;

2) EuroQol Foundation, The Netherlands

HOME ADDRESS 119/153 The Terrace Village, Soi Tiwanon 3,

Tiwanon Rd., Taladkwan, Muang,

Nonthaburi, Thailand

EMPLOYMENT ADDRESS Health Intervention and Technology

Assessment Program (HITAP), Ministry of

Public Health, Nonthaburi, Thailand

Position : researcher

Tel. 0-2590-4549

E-mail : [email protected]

HEALTH-RELATED QUALITY OF LIFE MEASURE (EQ-5D-5L ... · studies, mahidol univ. thesis / iv...

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Transcript of HEALTH-RELATED QUALITY OF LIFE MEASURE (EQ-5D-5L ... · studies, mahidol univ. thesis / iv...