Analytical Epidemiologic Study

Panithee Thammawijaya

Bureau of Epidemiology1

เป้�าหมายของการศึ�กษาทางระบาดวิ�ทยา

• DESCRIBE• ม�ผู้��ป้�วิยด�วิยโรคห�วิใจหลอดเล!อดมากน้�อยเพี�ยงใดใน้จ�งหวิ�ด

แห%งหน้�&ง• ผู้��ป้�วิยด�วิยโรคห�วิใจหลอดเล!อดเป้'น้สั�ดสั%วิน้เท%าไรใน้ผู้��หญิ�งและ

ใน้ผู้��ชาย• EXPLAIN

• ท,าไมผู้��ชายจ�งป้�วิยด�วิยโรคห�วิใจหลอดเล!อดมากกวิ%าผู้��หญิ�ง• การสั�บบ-หร�&เพี�&มควิามเสั�&ยงใน้การเป้'น้โรคห�วิใจหลอดเล!อดหร!อ

ไม%• PREDICT• ถ้�าสัามารถ้รณรงค0ให�คน้ใน้ช-มชน้เล�กสั�บบ-หร�&ได�เป้'น้ผู้ลสั,าเร1จ

จ,าน้วิน้ผู้��ป้�วิยโรคห�วิใจหลอดเล!อดรายใหม%ใน้ป้3หน้�าจะลดลงเป้'น้จ,าน้วิน้เท%าไร

Source: Morgenstern, 2001 (modified)

Measure of Frequency

Measure of Association

Measure of Impact

2

• CONTROL• มาตรการท�&เหมาะสัมสั,าหร�บช-มชน้ (ภายใต�ข�อจ,าก�ดต%างๆ) ค!อ

อะไร

3

การวิ�ดทางระบาดวิ�ทยา•Measure of Frequency: ขน้าดป้7ญิหา

– ควิามช-ก Prevalence – อ-บ�ต�การ Incidence

•Measure of Association: วิ�ดขน้าดควิามสั�มพี�น้ธ์0ระหวิ%างป้7จจ�ยก�บโรค–Risk Ratio, (Incidence) Rate Ratio

===> Cohort Study–Odds Ratio ===> Case – Control

Study–Prevalence Ratio, Prevalence Odds

Ratio ===> Cross-sectional Study

From Last Time…(1)If you want to count…

“State”Existing of… at a point of time

Prevalence(=New + Old cases)

“Event”Occurring of… during a period of time

Incidence(=New cases)

E.g. •Number of all DM cases a village in Jan 2009 = 120 •Proportion of current smokers in company on Jan 1st, 2010 = 15% of total employees

E.g. •Number of flu cases occurred in a village 2009 = 150•Proportion of new smokers in a company during Jan to May 2010 = 2% of non-smoker on Dec 31st, 2009

From Last Time…(2)

Point Prevalence

Prevalence

Period Prevalence

=

=

At time t1

During time t1-t2

= sick (new) = not sick= sick (old)

From Last Time…(3)

Incidence Proportion(Risk; Cumulative

Incidence )

Incidence

=

= During time t1-t2

Incidence Rate(Rate; Incidence Density)

= sick (new) = not sick= sick (old)

During time t1-t2

= person-time

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2x2 Table and Measure of Association(Count Data)

Disease Non disease Total

Exposed A B A+B

Unexposed C D C+D

Total A+C B+D A+B+C+D

Risk Ratio (RR) = [A/(A+B)] / [C/(C+D)] Odds Ratio (OR) = [A/C] / [B/D] = AD/BC Prevalence Ratio (PR) =

[A/(A+B)] / [C/(C+D)]

8

2x2 Table and Measure of Association(Person-Time Data)

No. of Cases Person-Time

Exposed A TE

Unexposed B TU

Total A+B TE+TU

Incidence Rate Ratio (IRR) = [A/TE] / [B/TU ]

Ratio Scale Measures and Theirs Relationships

OR OR

0 ∞1IRR IRRRR RR

The null value(no association)

weaker stronger

Causative Effect

weakerstronger

Protective Effect

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Causative FactorProtective Factor

Risk Factor

How epidemiologists work?1. Counting:

Counts cases or health events, and describes them in terms of time, place, and person

2. Dividing:Divides the number of cases by an appropriate denominator to calculate “rates”

3. Comparing:Compares these “rates” over time or for different groups of people

* Rate, in this case, simply means division of one number by another

DescriptiveEpidemiology

AnalyticEpidemiology

11

Classification epidemiological study

Observational Study

(natural exposure)

Experimental Study(exposure given by researcher)

การศึ�กษาเช�งพีรรณน้าDescriptive Study

(ไม%ม�กล-%มเป้ร�ยบเท�ยบ)

การศึ�กษาเช�งวิ�เคราะห0Analytic Study(ม�กล-%มเป้ร�ยบเท�ยบ)

Cross – sectional Case control CohortFrom: Ram Rungsin, modified

Case report Case series

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Case report: a hypertension

case in young adult

Case series: three

hypertension cases in

young adults

Cross – sectional study: a

hypertension survey

Cross – sectional study: HT

vs Salt consumption

Case – control study: HT vs

Salt consumption

Cohort study: HT vs Salt

consumption

Clinical trial: Beta blocker

vs Hypertension

Descriptive

AnalyticExperiment

ลำ��ดั�บชั้��นของก�รศึ�กษ�ท�งดั��นระบ�ดัวิ�ทย�

ลำ��ดั�บชั้��นของก�รศึ�กษ�ท�งดั��นระบ�ดัวิ�ทย�

From: Ram Rungsin

13

การศึ�กษาเช�งพีรรณน้า

ไม%ป้�วิย

ผู้��ป้�วิย เป้�าหมายMagnitude and

severityDistribution: Time,

Place, Person<<Hypothesis formulation>>

เป้�าหมายMagnitude and

severityDistribution: Time,

Place, Person<<Hypothesis formulation>>

สัน้ใจเฉพีาะกล-%มผู้��ป้�วิย

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การศึ�กษาเช�งวิ�เคราะห0

ไม%ป้�วิยป้7จจ�ย A?

ป้�วิยป้7จจ�ย A?

สัน้ใจท�:งกล-%มผู้��ป้�วิย

และไม%ป้�วิย

เป้�าหมายAssociation between Disease and Factor A

<<Hypothesis testing>>

เป้�าหมายAssociation between Disease and Factor A

<<Hypothesis testing>>

What Is the “Cause” of a Disease? (1)

1990 2010

Mr. A• 20-yrs male, Thai, farmer, etc.

Mr. A• 20-yrs male, Thai, farmer, etc.

Did pumpkin have an effect on the disease in Mr. A? Yes, causative effect.

Event actually occurred-observed

Counterfactual-not observed

What Is the “Cause” of a Disease? (2)

1990 2010

Mr. A• 20-yrs male, Thai, farmer, etc.

Mr. A• 20-yrs male, Thai, farmer, etc.

Did pumpkin have an effect on the disease in Mr. A? No. He is doomed.

Event actually occurred-observed

Counterfactual-not observed

What Is the “Cause” of a Disease? (3)

1990 2010

Mr. A• 20-yrs male, Thai, farmer, etc.

Mr. A• 20-yrs male, Thai, farmer, etc.

Did pumpkin have an effect on the disease in Mr. A? No. He is immune.

Event actually occurred-observed

Counterfactual-not observed

What Is the “Cause” of a Disease? (4)

1990 2010

Mr. A• 20-yrs male, Thai, farmer, etc.

Mr. A• 20-yrs male, Thai, farmer, etc.

Did pumpkin have an effect on the disease in Mr. A? Yes, protective effect.

Event actually occurred-observed

Counterfactual-not observed

• วิ�ดการเก�ดโรคใน้กล-%มต�วิอย%างกล-%มหน้�&งท�& “exposed” เป้ร�ยบเท�ยบก�บการเก�ดโรคใน้กล-%มเด�ยวิก�น้(คน้เด�ม)น้�:น้หากวิ%าพีวิกเขาไม%ได� exposed , หร!อ

• วิ�ดการเก�ดโรคใน้กล-%มต�วิอย%างกล-%มหน้�&งท�& “unexposed” เป้ร�ยบเท�ยบก�บการเก�ดโรคใน้ป้ระชากรกล-%มเด�ยวิก�น้(คน้เด�ม)น้�:น้หากวิ%าพีวิกเขาได� exposed

• สร�ป โดยหล�กการ การศึ�กษาเพี!&อค�น้หาสัาเหต- จะต�องเป้ร�ยบเท�ยบ

“actual outcome” vs. “potential outcome”

Causal Inference in Modern Epidemiology

19

Causal Inference in Modern Epidemiology• ใน้ทางป้ฏิ�บ�ต� เราไม%สัามารถ้สั�งเกตการเก�ดโรคใน้ภาวิะท�&เป้'น้

“counterfactual” หร!อ “the potential outcome” ได�• เราจ�งต�องท,าการเป้ร�ยบเท�ยบกล-%มต�วิอย%างท�&“exposed”

ก�บกล-%มต�วิอย%างอ!&น้แทน้ (Substitute population)

• กล-%มต�วิอย%างอ!&น้ท�&ใช�แทน้ได� จะต�องเป้'น้กล-%มต�วิอย%างท�&ม�ล�กษณะท�&เป้'น้ต�วิแทน้(represent) ของ กล-%มต�วิอย%างท�&“exposed”น้�:น้หากวิ%าไม%ได� “exposed”

• Validity of inference ข�:น้อย�%วิ%ากล-%มต�วิอย%างท�&น้,ามาเป้ร�ยบเท�ยบก�น้น้�:น้ (exposed and unexposed groups) สัามารถ้เป้ร�ยบเท�ยบก�น้ได� (comparability) มากน้�อยเพี�ยงใด

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What Is the “Cause” of a Disease?

1990 2010

Did pumpkin have an effect on the disease in population? Validity & Precision?

?

?Exposed group• age 15-25 yrs

Unexposed group• age 15-25 yrs

Analytic Epidemiological Study

Exposure DiseaseEffect?

•เป้�าหมายสั,าค�ญิของ Analytic study ค!อ การวิ�ด effect ของ exposure ท�&ม�ต%อโรคหน้�&งๆ•ใน้ Observational study ไม%สัามารถ้วิ�ด effect ได�โดยตรงเน้!&องจากกล-%มเป้ร�ยบเท�ยบอาจจะไม% Comparable•ใน้ทางป้ฏิ�บ�ต�จ�งวิ�ดได�แต%เพี�ยง ควิามสั�มพี�น้ธ์0(Statistical association)

Association?

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Cause? Risk factor?

•ใน้ทางป้ฏิ�บ�ต�ม�กจะไม%สัามารถ้ระบ-ได�แน้%ช�ดวิ%าสั�&งใดเป้'น้สัาเหต- (cause) ท�&แท�จร�งของโรคหน้�&งๆ เน้!&องจากข�อจ,าก�ดของควิามร� �(เช%น้ ด�าน้ช�วิวิ�ทยาหร!อกลไกการเก�ดโรค เทคโน้โลย�ใน้การวิ�ด ฯลฯ)•ใช�ค,าวิ%า Risk factor แทน้เพี!&อแสัดงถ้�งข�อจ,าก�ดด�งกล%าวิ

23

ก�รศึ�กษ�เชั้�งวิ�เคร�ะห์�• Cross – sectional Study

• Case – Control Study

• Cohort Study

Cross-sectional studyIn a cross-sectional study,

the measurementsmeasurements of exposure andeffect are made at the same time

24

25

ส��รวิจภ�วิะควิ�มดั�นโลำห์�ตส$งแลำะไขม�นในเลำ(อดั

• The Health Department of Hanoi City in 2000

• 1,000,000 Hanoi population

• ส�มภ�ษณ์�• เจ�ะเลำ(อดัวิ�ดั Cholesterol

• วิ�ดัควิ�มดั�นโลำห์�ต

26

• 60,000 = hypertension

• 200,000 = high blood

cholesterol

• Prevalence of HT = ? 600001000000 6, / , , = %

“ ควิามด�น้โลห�ตสั�งและไขม�น้ใน้เล!อด

ม�ควิามสั�มพี�น้ธ์0ก�น้หร!อไม% ”

ส��รวิจภ�วิะควิ�มดั�นโลำห์�ตส$งแลำะไขม�นในเลำ(อดั

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ส��รวิจภ�วิะควิ�มดั�นโลำห์�ตส$งแลำะไขม�นในเลำ(อดั

ID Age Sex -Hypertension

HighChol

1 18 M No Yes

2 36 M No No

3 50 F Yes Yes

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Defined PopulationDefined Population

Exposed:Have disease

Exposed:Have disease

Exposed:No diseaseExposed:

No diseaseNot Exposed:Have diseaseNot Exposed:Have disease

Not Exposed:No disease

Not Exposed:No disease

Gather Data on Exposure & Disease at the same timeGather Data on Exposure & Disease at the same time

ส��รวิจภ�วิะควิ�มดั�นโลำห์�ตส$งแลำะไขม�นในเลำ(อดั

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a b

c d

DiseaseNo Disease

Exposed

Not Exposed

ส��รวิจภ�วิะควิ�มดั�นโลำห์�ตส$งแลำะไขม�นในเลำ(อดั

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20000,

40000,

HT NoHT

High Chol.

Normal Chol.

1000000, ,

200000,

800000,

60,000,940000,

ส��รวิจภ�วิะควิ�มดั�นโลำห์�ตส$งแลำะไขม�นในเลำ(อดั

180000,

760000,

31

• High Cholesterol : HT Prevalence Rate = 20,000 / 200,000 = 10%

• Normal Cholesterol : HT Prevalence Rate = 40,000 / 800,000 = 5%

• Prevalence Ratio (PR) = 10% / 5% = 2

ส��รวิจภ�วิะควิ�มดั�นโลำห์�ตส$งแลำะไขม�นในเลำ(อดั

32

Prevalence Ratio (PR)• Prevalence Ratio = 10% / 5% = 2

• แป้ลวิ%า “ ผู้��ท�&ม�ภาวิะ high cholesterol ม�โอกาสัท�&จะ พีบวิ%าม�โรคควิามด�น้โลห�ตสั�งอย�%ด�วิยเป้'น้ 2 เท%าของผู้��

ท�&ไม%ม� high cholesterol”

PR จากการศึ�กษาแบบต�ดขวิาง สัามารถ้ใช�ป้ระมาณค%า “Risk Ratio” ถ้�าหาก

•ม�&น้ใจวิ%า Exposure เก�ดก%อน้ Disease (No temporal ambiguity)•Cases ท�&อย�%ใน้การศึ�กษาเป้'น้ต�วิแทน้ของ Incidence cases ท�:งหมด (ไม%ม� selective survival or prevalence-incidence bias)

33

Cross-sectional Studies

• Advantages:– quick, inexpensive– Useful for health administration and hypothesis formulation

• Disadvantages:– low prevalence due to

• Low incidence (rare disease)• short duration

– Uncertain temporal relationships– Selection Bias (Selective survival)– Information Bias (Recall bias)

Cohort study A study in which the

incidence proportion/rate of disease in 2 or more cohorts

is compared

34

35

A Roman CohortA Roman Cohort

Two centuries made one maniple and three maniples made up one cohort.

= A unit of 300-600 men in the ancient Roman army

36

= A group of persons who are followed over time

= A group of persons who are followed over time

“COHORT”in Epidemiology

“COHORT”in Epidemiology

37

Cohort Study• โดัยท�+วิไป ถื(อวิ-�เป.นก�รศึ�กษ�แบบ

ส�งเกตท/+ม/ควิ�มถื$กต�องส$งส�ดัในแง-ก�รห์�ควิ�มส�มพั�นธ์�ระห์วิ-�งป2จจ�ยก�บก�รเก�ดัโรค

• ใชั้�เวิลำ�ในก�รศึ�กษ�น�นท/+ส�ดั

• ใชั้�งบประม�ณ์ในก�รศึ�กษ�ม�กท/+ส�ดั

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การใช�ระบาดวิ�ทยาใน้การค�น้หาสัาเหต-ของ

การเก�ดโรค

RISK FACTOR (ป้7จจ�ยเสั�&ยง)•Cigarette

DISEASE (การเก�ดโรค)•Lung Cancer

Causeส�เห์ต�

Effectผลำ

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Person at-risk (without disease) at startPerson at-risk (without disease) at start

เก�ดโรคเก�ดโรค ไม%เก�ดโรคไม%เก�ดโรค เก�ดโรค เก�ดโรค ไม%เก�ดโรคไม%เก�ดโรค

ExposedExposed Not ExposedNot Exposed

40

Persons without the disease !!!!Not ExposedExpose

d Not Smoke#500

persons

Smoke#500

personsDise

aseNo

Disease

Disease

No Disea

seNo Lung Cancer

# 455

Lung Cancer

# 45

No Lung Cancer

# 499

Lung Cancer

# 1

1970

2001

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CA LungNo CA

Smoke

Not smoke

45455

1499

500

500

• Incidence of Smoker who develop Lung Cancer = 45/500• Incidence of Non -Smoker whodevelop Lung Cancer = 1/500• Risk Ratio of smoking for Lung Cancer = 45 • ผู้��ท�&สั�บบ-หร�&ม�โอกาสัเก�ดโรคมะเร1งป้อดมากกวิ%าผู้��ท�&ไม%สั�บ 45 เท%า

42

Risk RatioCA LungNo CA

Smoke

Not smoke

A B

C D

Risk Ratio = A/A+B C/C+D

A+B

C+D

43

•เล!อกป้ระชากรกล-%มท�&ย�งไม%เก�ดโรคแต%ม�โอกาสั

•ค�น้หา Exposed group และNon-exposed group

•ต�ดตามและวิ�ด incidence of disease outcome ท�:งใน้กล-%ม Exposed และ Non – exposed ระหวิ%าง ช%วิงเวิลาท�&ท,าการศึ�กษา

•ค,าน้วิณหา Risk Ratio หร!อ Incidence Rate Ratio

Conducting a Cohort Study

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Design 1: Prospective Cohort Study

PopulationPopulation

People withou

t diseas

e

People withou

t diseas

eUnexposedUnexposed

ExposedExposed

No diseaseNo disease

DiseaseDisease

No diseaseNo disease

DiseaseDisease

Time of Study BeginTime of Study Begin

Direction of inquiryDirection of inquiry

Sampling?

•If the study started before the disease occurred…“Prospective cohort study”

Cause Effect

Ex: A Study of Smoking and Lung Cancer(Prospective cohort study with person-time data)

No. of Case F/U time (person-year)

Smoking 90 30,526Non smoking 10 28,364

Total 100 58,890

Incidence rate in smokers = 90 / 30,526 = 2.9 per 1000 person-years

Incidence rate in non-smokers = 10 / 28,364 = 0.5 per 1000 person-yearsRate ratio = 2.9/0.5

= 5.8

Rate of developing the disease in smokers is 5.8 times of that in non-smokers 45

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Design 2: Retrospective Cohort Study

PopulationPopulation

People withou

t diseas

e

People withou

t diseas

eUnexposedUnexposed

ExposedExposed

No diseaseNo disease

DiseaseDisease

No diseaseNo disease

DiseaseDisease

Time of Study BeginTime of Study Begin

Direction of inquiryDirection of inquiry

Sampling?

•If the study started after the disease occurred…“Retrospective (Historical) cohort study”

Cause Effect

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Ex: An Diarrhea Outbreak in a Party(Retrospective Cohort study with count data)

Ill Not ill Total

Ate salad 150 50 200

Not eat 10 90 100

Total 160 140 300

Incidence proportion in exposed group = 150 / 200 = 75%

Incidence proportion in non-exposed group = 10 / 100 = 10%

Risk ratio = 75/10

= 7.5

Risk of developing the disease in exposed group is 7.5 times of that in non-exposed group

48

Cohort Studies - Advantages

• Can measure disease incidence • Can study the natural history • Provides strong evidence of casual

association between E and D (time order is known)

• Multiple diseases can be examined• Good choice if exposure is rare (assemble

special exposure cohort) • Generally less susceptible to bias

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Cohort Studies - Disadvantages• Takes time, need large samples, expensive• Not useful for rare diseases/outcomes • With prolonged time period:

– Exposures change during follow-up period

• Selection Bias (loss-to-follow up in pros. cohort or selective survival in retro. cohort)

• Information Bias (recall bias in retro. Cohort)

Case-control studyKey: it begins with people with the

disease (cases) and compares them to people without the disease (controls)

50

51

เป้'น้การศึ�กษาระบาดวิ�ทยาเช�งวิ�เคราะห0ชน้�ดหน้�&ง เป้ร�ยบเท�ยบระวิ%างผู้��ท�&เป้'น้โรค (Case) ก�บกล-%มต�วิอย%างผู้��ท�&ไม%เป้'น้โรค (Control) โดยท,าการเป้ร�ยบเท�ยบป้ระวิ�ต�ของล�กษณะการม�ป้7จจ�ยเสั�&ยงท�&ก,าล�งศึ�กษา ระหวิ%าง 2 กล-%ม 

Case – Control Study

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Non CasesFactor A

CasesFactor A

CasesFactor ACases

Factor ANon CasesFactor A

Non CasesFactor A

Case – control Study

53

DiseaseDisease No DiseaseNo Disease

ExposedExposed NotExposed

NotExposedExposedExposed Not

ExposedNot

Exposed

Design for a case – control

Study

54

Design of a case-control study

Case Population

Case Population

Controls(People without disease)

Controls(People without disease)

Case(People with disease)

Case(People with disease)

Not exposedNot exposed

ExposedExposed

Not exposedNot exposed

ExposedExposed

Non-casePopulationNon-case

Population

•Identify true case, and true non-case populations•Sampling fractions from case<>non-case•Determine exposure status by history

Time of Study BeginTime of Study Begin

Direction of inquiryDirection of inquiryCause Effect

What is “Odds”?Odds of an event with an occurrence probability of p is the ratio of p to (1-p)Odds = Probability of event

Probability of non-event= p/(1-p)

Probability = odds/(1+odds)

Odds of Exposure among cases = a/(a+c) = a/c c/(a+c)

a bc d

D

+ -

+E - Odds of Exposure among noncases = b/(b+d) = b/d

d/(b+d)

Head-to-Head = 9 : 18

For case-control study:

What is “Odds Ratio”?Odds Ratio (OR) = Ratio of two odds

In case-control study, Exposure OR = Odds of exposure among cases Oddsof exposure among noncases

= a/c = ad/bc b/d

Odds of disease among the exposed = a/(a+b) = a/b b/(a+b)

Odds of disease among the unexposed = c/(c+d) = c/d d/(c+d)

a bc d

D

+ -

+E -

For cohort study:

In cohort study, Disease OR = Odds of disease among the exposed Odds of disease among the unexposed

= a/b = ad/bc c/d

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2x2 Table and Measure of Association(Count Data)

Disease Non disease Total

Exposed A B A+B

Unexposed C D C+D

Total A+C B+D A+B+C+D

Risk ratio (RR) = [A/(A+B)] / [C/(C+D)] Odds ratio (OR) = [A/C] / [B/D] = [A/B] / [C/D]If disease is rare, then OR ~ RR

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Factors DiseaseCase – Control

CohortFactors Disease

Cause Effect

Case-Control V.S. Cohort

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Conducting a Case-control

Study• ค�น้หา “Cases”

• ท,าการค�ดเล!อก “Controls” โดยเล!อกจากกล-%มป้ระชากรท�&เป้'น้แหล%งก,าเน้�ดเด�ยวิก�น้ก�บ Cases ใน้การศึ�กษา (study base)

• วิ�ดล�กษณะการม�ป้7จจ�ยเสั�&ยง “ exposure ” ท�&สัน้ใจใน้กล-%ม cases และ controls

• เป้ร�ยบเท�ยบ exposure status ระหวิ%าง 2 กล-%ม

• ค,าน้วิณหา Odds Ratio

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Sources of Cases• Population-based (ผู้��ป้�วิยใน้ช-มชน้)

• identify and enroll all incident cases from a defined population• e.g., disease registry, defined geographical area, vital records

• Hospital-based (ผู้��ป้�วิยท�&มาร�กษา)– identify cases where you can find them

• e.g., hospitals, clinics.

– But……• issue of representativeness?• prevalent vs incident cases?

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Sources of Controls• Population-based Controls

• ideal, represents exposure distribution in the general population, e.g.,

– driver’s license lists (16+)– Medicare recipients (65+)– Tax lists– Voting lists– Telephone RDD survey

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Sources of Controls• Hospital-based Controls

– Hospital-based case control studies used when population-based studies not feasible

– More susceptible to bias

– Advantages• similar to cases? (hospital use means similar SES, location)• more likely to participate (they are sick)• efficient (interview in hospital)

– Disadvantages• they have disease?

– Don’t select if risk factor for their disease is similar to the disease under study e.g., COPD and Lung CA

• are they representative of the study base?

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Other Sources of Controls• Relatives, Neighbors, Friends of Cases

– Advantages• similar to cases wrt SES/ education/ neighborhood• more willing to co-operate

– Disadvantages• more time consuming• cases may not be willing to give information?• may have similar risk factors (e.g., smoke, alcohol, golf)

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Case : Control Ratio • อ�ตราสั%วิน้ของ case : control โดยท�&วิไป้อย�%ระหวิ%าง 1:1 ถ้�ง 1:4

• ถ้�าจ,าน้วิน้ case เท%าเด�ม

– การเพี�&มจ,าน้วิน้ control จะช%วิยเพี�&ม precision ของ Odds ratio

– แต%การเพี�&มจ,าน้วิน้ control ให�มากกวิ%า 4 ต%อ 1 case พีบวิ%าไม%ได�เพี�&ม precision

มากเท%าไรและอาจไม%ค-�มก�บต�น้ท-น้ท�&เพี�&มข�:น้

• ถ้�าจ,าน้วิน้รวิมของ Case ก�บ Control คงท�&

– อ�ตราสั%วิน้ 1:1 จะท,าให�ได� precision ของ Odds ratio มากท�&สั-ด

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Cases

Controls

Lung

Cancer

#50 cases

Lung

Cancer

#50 casesSmoke

# 45

Not

Smoke

# 5

NO Lung

Cancer

#200

controls

NO Lung

Cancer

#200

controls

ExposedUnexposedSmo

ke

# 99

Not

Smoke

# 101

ExposedUnexposed

Ex: Smoking and Lung Cancer

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Smoke

Not smoke

50 95

10 99

CA LungNo CA

Cohort Study

Case – Control Study

• Do not have incidence in exposed & incidence in non exposed • Cannot calculate the RR directly

CA LungNo CA

Smoke

Not smok

e

500 9,500

100 9,900

10,000

10,000

RR = (500/10000)/(100/9900) = 5

OR = (50/10)/(95/99) = 5.2

OR = (500/9500)/(100/9900) = 5.2

Ex: A Food Poisoning in a School (1)(Case-control study)

Case Control

Ate ice cream 40 17

Not eat 15 38

Total 55 55

Odds of eating ice cream in cases = (40/55) / (15/55) = 40/15

= 2.67

Odds of eating ice cream in control = (17/55) / (38/55) = 17/38

= 0.45

Odds ratio = 2.67 / 0.45 = 5.9 67

Ex: A Food Poisoning in a School (2)(Case-control study)

How to interpret odds ratio of 5.9 ???

In conventional case-control study: case vs. non-case

1. Study cases represent cases in population2. Study control represent non-case in population

>>> OR of 5.9 means “Odds of disease among the exposed is 5.9 times of that among the unexposed”

>>> OR ≈ RR

In population-based case-control study: • With case-cohort sampling

• With density sampling

>>> OR = RR

>>> OR = IRR

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If 1.+2.+ 3. Rare disease

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• Quick and cheap (relatively)– so ideal for outbreaks

• Can study rare diseases (or new)

• Can evaluate multiple exposures

Case-control Study - Advantages

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Case-control Study - Disadvantages• uncertain of Exposure-Disease relationship (esp.

timing)• cannot estimate disease incidence• inefficient if exposures are rare• Selection Bias

– Much worry about representativeness of controls– selective survival if not using incidence cases

• Information Bias (recall bias)

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Acknowledgement

• Dr. Chuleeporn Jiraphongsa• Dr. Ram Rungsin• Dr. Darin Areechokchai• Dr. Mathew J. Reeves

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