i

COST BENEFIT ANALYSIS OF PERSONALIZED HEALTHCARE

DELIVERY FOR BREAST CANCER PATIENTS

A Thesis Presented

By

Shujun Li

to

The Department of Mechanical and Industrial Engineering

in partial fulfillment of the requirements

for the degree of

Master of Science

in the field of

Industrial Engineering

Northeastern University

Boston, Massachusett

December 2015

ii

ACKNOWLEDGMENTS

I’d like to sincerely thank Prof. Sagar Kamarthi, my thesis advisor, for his patient

guidance and support. Thanks to Dr. Selen önel for previous work and suggestions on

the subject.

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TABLE OF CONTENTS

List of Tables……………………………………………………………………….….v

List of Figures……………………………………………………………………..…vii

ABSTRACT…………………………………………………………………………viii

Chapter 1 Problem Introduction……………………………………………………….1

1.1 Breast cancer overview……………….………………………………………...…1

1.2 Breast cancer treatment………………………………………..…………………..3

1.3 Motivation…………………..…………………………………………………..…6

1.4 Problem overview……………………………………………………………...….7

1.5 Approach ………………………………………………………………………… 8

Chapter 2 Literature Review…………………………………………………………11

2.1 From standardization to personalization…………………………………………11

2.2 Prognosis and risk control……………….……………………………………….12

2.3 Cost effectiveness and cost benefit studies………………………………………12

2.4 Modeling methods…………….………………………………………………….14

Chapter 3 Process Descriptions……………………………………………………....17

3.1 Problem statement………………………………………………………………..17

3.2 Model building……………….…………………………………………………. 18

3.2.1 Population……………………………………………………………………...18

3.2.2 Medical decision tree…………………………………………………………..21

3.2.3 Markov model…………………………………………………...……………..22

iv

Chapter 4 Simulation Model…………………………………………………………25

4.1 Modeling details………………………………………………...……………..…25

4.2 Results………………………………………………………………………...….30

4.3 Verification………….……………………………………………………………32

4.4 Validation………………………………………………………………………...32

Chapter 5 Design of Experiments…………………………………………………....34

5.1 Control factors……………………………………………………………………34

5.2 Full factorial 33 design………………………………….……………….…..…35

Chapter 6 Results and conclusions…………………………………………………...49

REFERENCES…………………………………………………………………….…50

APPENDIX…………………………………………………………..……………....54

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LIST OF TABLES

Table 1.1 Breast cancer type [34]……………………………………………………..2

Table 1.2 Role of personalized medicine and industrial engineering in healthcare

[35]…………………………………………………………………………………….7

Table 3.1 Age-adjusted SEER incidence rate of breast cancer from 2001 to 2011

[37]…………………………………………………………………………………...20

Table 3.2 Age-adjusted SEER incidence rate of breast cancer by race from 2001 to

2011 ………………………………………………………………………………….20

Table 4.1 Fixed probabilities in the model…………………………………………..27

Table 4.2 Life years gained from different therapy choices…………………………28

Table 4.3 Probabilities in the Markov chain…………………………………………29

Table 4.4 SEER estimated prevalence percent on Jan 1st, 2011 in the previous 19

years…………………………………………………………………………...……. 31

Table 4.5 Six replication of conventional base case………………………………....33

Table 4.6 Six replication of personalized base case…………………………………33

Table 5.1 Factors and factor levels…………………………………………………..34

Table 5.2 Output of average cost per patient………………………………………...36

Table 5.3 Analysis of Variance for Average Cost Per Patient, using Adjusted SS for

Tests………………...…..………………………………………...……………….....37

Table 5.4 Output of average life years gained…………………………………….....39

Table 5.5 Analysis of Variance for Average Life Years Gained, using Adjusted SS

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for Tests………………………………………………………………………………40

Table 5.6 Output of average cost per patient……………………………………...…42

Table 5.7 Analysis of Variance for Cost Per Extra Life Year Gained, using Adjusted

SS for Tests…………………………………………………………………………..43

Table 5.8 Output of average cost per patient………………………………………...45

Table 5.9 Analysis of Variance for Cost Effective Ratio, using Adjusted SS for

Tests……………………………………………………………………………….....46

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LIST OF FIGURES

Figure 1.1 Breast cancer usual pathways from public health program [36]………….4

Figure 1.2 Breast cancer sub grouped by molecular information [6]………..……….5

Figure 2.1 Markov model presented by Carter et al. [22]…………………...………15

Figure 3.1 SEER Incidence rates 2002-2011 [37]………………………………..…21

Figure 3.2 Prime state transitions for Markov model…………………………….….23

Figure 4.1 The simulation logic……………………………………………………..25

Figure 5.1 Normal plot of Residuals for Average Cost Per Patient…………………37

Figure 5.2 Main effects plot matrix for Average Cost Per Patient…………………..38

Figure 5.3 Interaction plot matrix for Average Cost Per Patient………………….…38

Figure 5.4 Normal plot of Residuals for Average Life Years Gained………………40

Figure 5.5 Main effects plot matrix for Average Life Years Gained……………..…41

Figure 5.6 Interaction plot matrix for Average Life Years Gained……………….....41

Figure 5.7 Normal plot of Residuals for Cost Per Extra Life Year Gained………....43

Figure 5.8 Main effects plot matrix for Cost Per Extra Life Year Gained………..…44

Figure 5.9 Interaction plot matrix for Cost Per Extra Life Year Gained………….…44

Figure 5.10 Normal plot of Residuals for Cost Effective Ratio……………………..46

Figure 5.11 Main effects plot matrix for Cost Effective Ratio…………………...…47

Figure 5.12 Interaction plot matrix for Cost Effective Ratio………………………..47

Figure B1 Traditional breast cancer decision tree………………………………...…55

Figure B2 Personalized breast cancer decision tree…………………………………57

Figure C Anylogic screenshots…………………………………………………...…61

viii

ABSTRACT

Breast cancer is a major field of study in healthcare due to the high prevalence of

cancer and high death rate of patients. Personalized treatments have been introduced

to improve the breast cancer treatment process. In this thesis, a personalized

healthcare delivery model is presented to provide better treatment options from the

perspective of payers and patients.

Given that treatment options take place as events in time, a discrete event simulation

model is constructed in Anylogic. Patients are divided into six age-based subgroups.

As they enter the model at a given rate, costs are generated based on the treatment

they are given. Treatment then help to prolong patients’ life years. A Markov model is

used to decide the type of recurrence. Then further treatment can be delivered

according to patients’ recurrence type. Personalized breast cancer treatment and

conventional breast cancer treatment are compared as two base case. The results

indicate that personalized treatments provide better healthcare delivery by reducing

less costs and extending life years.

The full factorial 33 design shows the level of personalization is the most significant

factor in the cost effectiveness of breast cancer treatment. It is concluded that the

healthcare delivery system will be improved by increasing the personalization level,

decreasing the genetic cost, and prolonging the time interval for checking the

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recurrence. This thesis provides patients and payers an economic view from which to

look at personalized medicine in breast cancer. The same method can be generalized

and applied to other cancer fields as well.

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Chapter 1 Problem Introduction

1.1 Breast cancer overview

Cancer is one of the leading causes of death among women; it is one of the most

common type of cancers. According to the 2014 cancer statistics [34], the death rate

caused by breast cancer among women is exceeded only by lung cancer. Since 1975

breast cancer has the highest incidence rate relative to other types of cancers among

females. It is estimated that about 1 in 8 (12%) women in the United States will

develop invasive breast cancer during their lifetime. The estimate for 2014 from

American Cancer Society [34] shows that approximately 40,000 women die from

breast cancer. Therefore, reducing the mortality of breast cancer has always been a

popular topic among the healthcare researches; finding a cost effective method is

essential to get the best value out of the existing resources.

According to the American Cancer Society [34], breast cancer is a malignant tumor

that starts in the cells of the breast, and later on spreads to surrounding tissues or

metastasizes to distant areas of the body. In general, breast cancer can be classified as

non-invasive and invasive according to the tumor size and the area to which it spreads.

Non-invasive breast cancer is an abnormal growth of cells contained within the area

in which they started; these cancer cells would not have invaded into surrounding

breast tissue yet. Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer

referred to as Stage 0. (“In situ” means in place.) Although DCIS and lobular

carcinoma in situ (LCIS) sound similar, LCIS is not considered breast cancer. LCIS is

a risk factor for breast cancer. When breast cancer cells spread to surrounding breast

tissue from the ducts or lobules, the cancer is considered invasive. This increases the

chance for cancer cells to spread to the lymph nodes. Inflammatory breast cancer

(IBC) and Paget’s disease of the nipple are two rare types of invasive breast cancer.

Other less common forms of invasive breast cancer are medullary, mucinous,

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papillary and tubular carcinoma. Invasive breast cancer is not the same as metastasis.

Metastasis occurs when breast cancer cells break away from the breast tumor and

spread to other organs of the body through either the blood stream or the lymphatic

system.

Table 1.1 Breast cancer types [34]

In addition to the type of a breast cancer tumor — non-invasive or invasive — doctors

Type Stage Spread Size

Non-invasive 0 Local Tiny cluster of cancer cells in a breast duct (in situ)

The tumor is no more than 2 cm (3/4 inch) in diameter

The cancer hasn't spread to lymph nodes.

The cancer hasn't spread outside breast.

Local/

Regional

The tumor is 2 to 5 cm (3/4 to 2 inches) in diameter. The

cancer may or may not have spread to underarm (axillary)

lymph nodes.

LocalThe tumor is more than 5 cm (2 inches) in diameter but

the cancer hasn't spread to axillary lymph nodes.

The tumor is less than 2 cm (3/4 inch) in diameter, but the

cancer has spread to no more than three axillary lymph

nodes.

No tumor is found in the breast, but breast cancer cells are

detected in no more than axillary lymph nodes.

The tumor is larger than 5 cm (2 inches), with cancer cells

that have spread to axillary lymph nodes. However, the

nodes aren't attached to one another.

The tumor is smaller than 5 cm (2 inches), but the cancer

has spread into nearby lymph nodes and the nodes are

growing into each other or the surrounding tissue

(stroma).

The tumor is smaller than 5 cm (2 inches), but the cancer

has spread to the lymph nodes above collarbone.

Inflammatory breast cancer is a form of cancer in which

there may be no lump or mass felt in the breast. In

inflammatory breast cancer, cancer cells block the

lymphatic vessels in breast skin, causing swelling, redness,

and ridged or dimpled skin.

Invasive adenocarcinoma of the breast

Metastatic breast cancer

The most advanced form of breast cancer

Breast cancer cells have spread to other areas of body.

Breast cancer most often spreads to the bones, brain, liver

and lungs.

Early and

locally

advanced or

invasive

Advanced or

metastatic

I Local

II

III Regional

IV Distant

Regional

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also look at the tumor characteristics such as the sizes and the spread. Table 1.1

summarizes breast cancer types, stages in detail and explains the corresponding

spread and sizes. The sizes and spread of breast cancer are important characteristics in

determining the stage of breast cancer. The stage determines the prognosis (expected

outcome) and treatment options. The breast cancer Classification System of

Malignant Tumors, TNM, is usually used to classify the breast cancer stages, which

makes it easy to quantify the seriousness of the disease; T represents the size of the

tumor; N describes the lymph nodes involvement; M indicates the presence of distant

metastasis. When the tumor size is quite small, the disease displays no symptoms, so

breast cancer screening plays a significant role in detecting the disease at an early and

treatable stage. It is recommended [35] that women should have mammography and

clinical breast examination (CBE) every year after the age of 40, and magnetic

resonance imaging (MRI) every year when they have a relatively high risk of getting

breast cancer. The risk depends on the family history, age, hormonal factors, and some

other factors.

1.2 Breast cancer treatment

The conventional breast cancer treatment often involves cancer removal surgeries

combined with radiation therapy, chemotherapy, hormone therapy, and target therapy.

Breast cancer removal surgeries include lumpectomy, partial or total mastectomy. The

resection ranges from the removal of cancer tissue to lymph nodes and the whole

breast in extreme cases. Patients can also decide whether or not they will receive a

breast reconstruction surgery after mastectomy. Sometimes, neo-adjuvant therapies

are performed before the surgery to avoid possible overtreatment operation. After the

surgery, adjuvant therapies are usually given to patients with no detectable cancer

symptons to prevent recurrence. Figure 1.1 summarizes the usual clinical pathways

the breast cancer patients go through based on the public health programs [36].

Generally, the physicians use these clinical pathways to make the medical decisions.

Thus, the pathways are developed based on the results of clinical trials to standardize

the effective treatment process.

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Figure 1.1 Typical breast cancer pathways followed by public health programs [36]

It is generally known that different patients have different responses to the same type

of treatment. Finding an effective breast cancer treatment path would benefit both

patients and medical providers. Thanks to the development of genomic technologies

and medical services, in recent decades, a new concept of “personalized medicine”

has drawn the attention of researchers in terms of finding the most tailored medicine

for treating breast cancer. Derived from “patient-as-a-person” initiative, personalized

medicine utilizes the genetic information of patients’ tumor to make approved,

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tailored diagnosis, treatment, and prognosis. In this personalization approach, patients

are divided into subgroups that have similar medical conditions based on their genetic

information, which increases the possibility of them responding positively to a certain

treatment. However, personalized medicine is not precise as each patient has requires

a customized diagnosis, cure and care. The benefits of personalized medicine include

better prevention of disease recurrence, eligibility to choose the optimal therapy, and

improved medical decisions.

Additionally, the use of molecular tests and biomarkers has come a long way with the

increasing need for providing personalized treatment. A biomarker is a measurable

characteristic indicator of pathogenic processes. Biomarkers relevant to breast cancers

include estrogen receptor (ER), progesterone receptor (PR), and human epidermal

growth factor receptor 2 (HER2). Some of the treatment methods have already been

put into practice after adequate trials. Currently, they are frequently used in medical

care. Molecularly distinct subgroups are usually referred as luminal A, luminal B,

normal-like, HER2-like, basal-like, and claudin low (also known as triple negative) as

shown in Figure 1.2.

Figure 1.2 Breast cancer sub grouped by molecular information [6]

In current practice, molecular tests are widely applied to risk assessment in

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early-stage breast cancer, gene expression profiling, mutational analysis and the

prediction of patients’ resistance or sensitivity to a given therapy. In medical practice,

the “one-size-fits-all” principle is no longer the answer. Breast cancer treatment is

evolving from standardization to personalization gradually.

There are still challenges to the full realization of personalized medicine. First, there

is the ethics problem. Ideally, therapists want to build a database containing all the

patients’ genetic information for future reference. However, there are privacy issues.

Genetic information is highly personal and involves family history. Physicians need to

get the authority to retain this data from the patients themselves without violating

confidentiality. Secondly, a personalized healthcare delivery system requires sufficient

recording of patient trials. Personalized medicine overall incurs cost in building and

maintaining the database, and in the development of new biomarkers, and genetic

technology. At present, the cost of personalized medicine in breast cancer is very

high.

1.3 Motivation

Both the high incidence rate and death rate make it critical to improve the quality of

the breast cancer treatment. This work applies industrial engineering tools to

personalized breast cancer diagnosis, treatment, and care. This will enable

collaboration between healthcare providers and engineers to improve the US and

global healthcare. Originating from manufacturing sector, lean approach offers a

balance between standard healthcare and personalized healthcare. On the other hand,

personalized healthcare with mass customization is totally patient centered; it delivers

healthcare on an individual level. As illustrated in Table 1.2, industrial engineering

methods help providers and patients make better medical decisions to improve drug

performance, reduce medical errors, maximize the quality and safety of medical

operations, and minimize the total cost. Without an optimal process, extra costs will

be incurred due to poor quality of medical care, which adds to the financial burden on

the government, insurance companies, and patients, since medical resources are

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limited. Despite the costs, the efforts to find optimal treatment plans can save the time

spent in the healthcare system as well.

Table 1.2 Role of personalized medicine and industrial engineering in healthcare [35]

Feature Healthcare Today Healthcare with Lean

Approach

Personalized Healthcare

with Mass

Customization

Approach

Goal

Standard healthcare

service with negligible

variety regardless of

patient condition

High quality and

highly effeicient

healthcare that is

optimized for each

segment of patients

High quality and highly

responsive healthcare that

is individualized for each

patient

Process

Providers maintain

extra capacity,

resources, and supplies

to cater patients' needs

Providers adopt lean

practices to offer

efficient and cost

effective patient care

Providers adopt flexible

and responsive processes

to offer individualized

care

Outcome

Healthcare with no

concern for patient

satisfaction, cost or

efficiency

Cost effective and

efficient healthcare

with some

consideration for

patient satisfaction

Cost effective and

responsive healthcare

with focus on maximum

patient satisfaction

1.4 Problem overview

Medical providers and researchers are dedicated to creating an ideal healthcare

delivery system. A good healthcare delivery system should be patient-centered and

cost-effective, where the medical decisions are transparent and patients’ information

is confidential. A good healthcare delivery system answers questions regarding which

treatment is good for a patient. The study will not focus on the design of the

healthcare delivery system, but use the National Comprehensive Cancer Network

clinical practice guidelines as a reference, creating three relatively simplified delivery

paths. They include different degrees of breast cancer treatment personalization.

Cost benefit and cost effectiveness analysis are popular tools in the field of healthcare

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research. Before a new medical intervention is put to a medical trial, the payer would

like to know whether it is economical in practice and how much toxic it is; therefore

this study conducts cost-benefit and cost-effectiveness analysis. Because personalized

medicine is still a relatively new approach for breast cancer treatment, this study will

focus on the breast cancer treatment process by combining the existing practices with

personalized medicine to check its effectiveness and practicality. There are also

multiple factors such as clinical outcomes, direct costs, and patients’ willingness to

pay are taken into account.

Overall, the objective of the study is to evaluate the personalized breast cancer

diagnosis treatment and care, to find the most cost-effective way of treating female

breast cancer from patients’ perspective. It investigates a methodology to study

operational and strategic issues in healthcare system.

1.5 Approach

After investigating different simulation methods, the model is formulated as a discrete

event model. At first, an agent-based model is considered because it allows

communications between entities, and the study of the model performance at a

microscope level. If patients are assumed to be the agents, they can have their own

syndromes and have their own decisions about medical intervention, such as whether

to accept reconstruction surgery after a mastectomy. The model approximates the real

situation, but it may require a whole lot of information about the patient’s family

history and physical exam results. Due to the limited data, most of the data are

obtained as probabilities for certain patient groups. Therefore, the discrete event

simulation replaces agent-based modeling. The treatment of the patients will follow

the time sequences and take place as events.

As the entities enter the system, patients are divided into subgroups according to their

age. Age affects the incidence rate of breast cancer. Studies show that women aged 50

and older have higher risk of developing breast cancer than women in other age

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groups. Conditions of female breast under tissues are also related to age and

menopausal status. Patients with different ages may have different reactions to the

same type of treatment. In addition, age affects the death rate of patients. Thus, age is

an attribute of the entities.

In the main part of the model, a guideline of breast cancer treatment from the National

Comprehensive Cancer Network is used as reference for the decision tree model; it

serves the same function as the current healthcare delivery system. Then the decision

process based on patients’ genetic information is developed in the model to fulfill the

requirement of personalization. The base model is established using the combination

of the traditional and the new breast cancer delivery systems.

Computer simulation is adopted in this thesis for modeling. Among all the industrial

engineering tools, simulation is frequently used to build models to understand the

target system behavior, and to evaluate different strategies of operation without

actually building a real system. The simulation model is built using Anylogic software,

which allows interface with external functions, and objects written in Java language.

Anylogic is a powerful simulation software, which supports system dynamics,

agent-based, discrete event and hybrid modeling. It also has 3D animation features to

allow model visualization.

In the simulation, a Markov model is applied to track the progression of the patients’

relapse. Patients receive different treatments in different types of relapses. The five

states of the patients are disease free, local recurrence, regional recurrence, and

systematic diseases. It is quite common that patients will have repeat recurrence.

Cost effectiveness is a technique for economic evaluation. The costs and survival rate

are used to calculate the cost effectiveness ratio. In this study, treatment strategies

with and without personalization are compared in costs and life years gained. The

costs of obtaining genetic information vary over time, which may have major impact

10

on the results especially in the personalization case. Design of experiments is

therefore employed to track those impacts on the model on the cost effectiveness

ratio.

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Chapter 2 Literature Review

This chapter reviews studies about the evaluation of personalized breast cancer. There

are articles that report breakthroughs in the application of gene-based treatment.

Researchers are finding the balance point between finding the tailored medical

pathways and utilizing the limited resources. Cost effectiveness studies are often

conducted when a new intervention needs to be evaluated in clinical trials to see the

feasibility. Another set of the studies emphasizes the effect of using different research

attributes such as different age group of patients on cost of study. Overall, different

studies provide insights into the personalized breast cancer problem from different

perspectives.

2.1 From standardization to personalization

In the past few years, the treatment of breast cancer has taken a huge step from

standard approach to personalization. As the need grows for more precise breast

cancer treatment, personalized medicine strives to find new approaches to treating

cancers such as lung cancer, breast cancer, leukemia, melanoma and colon cancer.

Beaston [1] is among who put forward the concept of personalized medicine in breast

cancer treatment. After observing patients’ cancer cases, he suggested that the

etiology of cancer should be traced locally not parasitically. After him, many

researchers studied the feasibility and benefits of personalized medicine in breast

cancer. Russell [2] systematically described the usual five subtypes of breast cancer as

basal-like, HER2+, (see Appendix for definition of abbreviations) normal breast like,

luminal A, luminal B and triple negative. The personalized approach to treat each type

of breast cancer is already followed in clinical practice. There are target therapies

towards the biomarkers BRCA1/2, Estrogen receptor, HER2/neu receptor and

Oncotype DX, MammaPrint gene profile as for breast cancer recurrence. Besides

these, new breakthroughs are also happening. Ellis [4] studied the new omic profiling

to extract valuable information from the given data set. New trails [5][6][7] are being

concluded to validate and implement new personalized pathways. Drier et al. [8]

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introduced an algorithm to determine a deregulation score for a gene set for

transferring genetic information to pathway information. In that way, pathways can

become more personalized and associated closely with patients’ information. The

most recent studies discuss the cell proliferation on the Nano level.

Although personalized medicine can bring many benefits, there are still obstacles to

delivering personalized therapy. Weldon et al. [11] conducted qualitative research by

interviewing medical providers, insurance companies and patients in the Midwest of

the United States. Although they assumed that medical receivers are acknowledged

with the gene tests BRCAnalysis and Oncotype DX in the first place, they came to the

conclusion that the major barriers to personalized treatment of breast cancer exists in

the poorly coordinated diagnostic testing and the reimbursement structure. Rivenbark

et al. [9] and Coleman [10] reviewed the challenges and opportunities in breast cancer

treatment personalization based on a molecular and cellular basis. They agreed that

each patient’s breast cancer is a unique, even if it falls into the same molecular

classification as others. The obstacles to personalized breast cancer treatment include

collecting the drug response data for various types of the diseases and the ethnic

questions. These add the difficulty in finding the tailored method. Although

Rivenbark et al. [9] presented a potential solution though next-generation sequencing

technology as a way to store the data, it requires further development in molecular

technologies.

2.2 Prognosis and risk control

Prior to the diagnosis of breast cancer, risk assessment is important to effective

prevention. Risk analysis can be applied in prognosis and risk reduction in treatment.

This line of research includes the family genetic risk assessment, risk reduction

therapy, and risk management. Gail [12] examined the use of absolute risk models,

which help to design new trials to prevent the disease, assess the risk factor

distribution, implement prevention strategies and decide the allocation of public

resources. His model can also be applied to making decisions regarding therapy

13

strategies by predicting the potential clinical outcomes. Ko et al. [13] discussed

several models, advocating for a personalized approach for breast cancer risk

reduction. Researchers take risk models as tools to weigh the risks and benefits of

medical strategies. Individualization is always the optimal solution to maximizing the

benefits while minimizing the risks.

There are online decision making tools like Adjuvant!Online, that provide treatment

options and risk factors. Ozanne et al. [15] designed and evaluated the similar tool

called BreastHealthDecisions.org by comparing the patient groups performance.

Mandeblatt et al. [16] reported that personalized breast screening assists the

development of risk forecasting. Inaji [16] and Sabatier et al. [18] discussed

personalization of breast cancer management. Even though some methods of

personalized breast cancer treatments are still under debate, they remain a trend

leading to a new medical revolution.

To effectively deliver the new method, patients’ own opinions cannot be neglected.

Scherer et al. [14] investigated women’s views about personalized breast cancer risk

statistics. She formulated that most women believed in statistics if they have been

explained well. Thus, if the statistics are more acceptable, they can have a positive

impact on the decision making in the medical process.

2.3 Cost effectiveness and cost benefit studies

Economics is an important aspect of breast cancer treatment. The cost-of-illness

studies focus mainly on the costs patients incur in the process of treatment rather than

on the effectiveness of the treatment itself. The costs regarding the breast cancer

treatment can be total costs or costs associated with the particular diagnosis. Campbell

et al. [19] reviewed cost-of-illness studies that estimate the costs of breast cancer.

They gave an estimation of $20,000 to $100,000 per patient over their lifetime based

on the data from 1984 to 2003. This review categorized studies based on perspective

of the study, the year of the study, age of the patients, and the severity of the disease;

14

they separated the costs of initial treatment, continuing treatment, and terminal care.

They introduced control groups, summarized different perspectives, and reported

statistical uncertainty for future researchers. Since screening is an important means to

detect breast cancer, Johnston [20] concentrated on the costs of breast screening for

both breast cancer patients and non-breast cancer individuals. The author used the

Nottingham prognostic index [20] and a Markov model with the five states

representing five prognostic subgroups of patients. They reported that the estimated

total future costs varied from $16,000 to $18,000. Lauzier et al. [21] developed a new

method of estimating cancer costs. They broke down the costs and used interview

responses to increase the validity of the estimation. The main conclusion is that the

costs go up when the stage of the breast cancer become more severe, and the costs are

highly associated with the age of the patients.

2.4 Modeling methods

There are modeling methods associated with the patients’ stages of the disease.

Markov model, analytic hierarchy process (AHP), and the analytic network process

(ANP) are compared in the same case of an elderly patient with early-stage breast

cancer by Carter et al. [22]. Markov chain is the most widely applied model; it shows

patients’ change of states over time, and therefore allows the estimation of the patient

life span. AHP and ANP models allow decision making more subjectively. AHP

model weighs information from both physicians and patients in an interactive way to

build a hierarchical tree from the objective level to the detailed elements. ANP serves

the same function as AHP, but the former uses element clusters instead of a strict

hierarchical tree. Jerez-Aragones et al. [23] developed a decision-making tool using a

new CIDIM algorithm to foretell the breast cancer relapse. Chan [24] built a

continuous Markov model to determine the optimal interval for breast cancer

screening. The model implemented different transition rates among different ages.

The equations are as follows:

𝑃{𝑇(𝑎 + ∆𝑎) = 𝑘|𝑇(𝑎) = 𝑘} = 1 − 𝜆𝑘(𝑎) + 𝑜(∆𝑎), 𝑘 = 0, 1

𝑃{𝑇(𝑎 + ∆𝑎) = 𝑘 + 𝑛|𝑇(𝑎) = 𝑘} = 𝜆𝑘(𝑎) + 𝑜(∆𝑎), 𝑘 = 0, 1

15

𝑃{𝑇(𝑎 + ∆𝑎) = 𝑗|𝑇(𝑎) = 𝑘} = 0, 𝑗 < 𝑘

The model considers only three stages: no cancer, preclinical cancer, and clinical

cancer. The corresponding states are denoted as 0, 1 and 2. The paper explained the

statistic model in detail and calculated the transition rate with the observed

probabilities.

Figure 2.1 Markov model presented by Carter et al. [22]

An important question being asked before a clinical pathway is put in practice is “Is it

affordable for both the provider and clients in the system?” This is where the cost

effectiveness study comes into the picture. The most popular perspective of the

research is from the payer. Additionally, since the advanced breast cancer involves

many more elements than the early stage breast cancer, the latter is often the focus of

research studies. Griffiths [25] explained the whole concepts and methodology of cost

effectiveness and discussed the methodology for cost benefit analysis. He used the

breast cancer screening example to illustrate how the economic evaluation works. The

paper divided the costs into direct and indirect costs. Then sensitivity analysis is

performed on both of the two screening and non-screening programs.

Most current studies are about cost benefit and effectiveness analysis on a certain type

of medical intervention in the treatment. Chang et al. [26] constructed an overall

frame of cost effective study when an intervention need to be evaluated. There are

cost-effectiveness studies regarding the genetic profiling at the early stage of the

breast cancer. Vanderlaan et al. [27] tested the economic effect of 21-gene assay in the

16

early stage breast cancer. They adopted was deterministic model to get the estimation

of costs and adjusted life quality years. The intervention was implemented

accompanied by the setting of other adjuvant therapies. Yang et al. [28] used a

decision analytic model to evaluate the gene expression profiling and adjuvant

therapies for the early stage breast cancer. They included a Markov model with

10-year time horizon. Both of the studies show that the genetic profiling methods,

such as 21-gene assay and 70-gene signature (MammaPrint), which have already been

put into practice, are more cost effective than the traditional methods. Other newly

developed technologies still have room to improve their economic performance. Other

genes examined in mammography screening are BRCA1 and BRCA2 [29]. Other cost

effectiveness studies focus on adjuvant therapy such as trastuzumab, ixabepilone plus

capecitabine in the early stage, advanced breast cancer, recurrence or follow-up

treatment.

Cost benefit studies are conducted after cost effectiveness studies. After identifying

the system with the highest potential economic benefits, one has to decide to what

extent should the intervention be implemented to benefit the most. The cost benefit

studies, unlike the cost effectiveness studies, usually cover a wide scope without

being limited to a certain stage. Lux et al. [30] took the adjuvant settings and Oltra et

al. [31] set the analysis under the whole follow-up program. Personalization of

treatment is a driving force in today’s medical practice, making cost-effectiveness and

cost-benefit studies more demanded.

17

Chapter 3 Process Descriptions

3.1 Problem statement

The objective of this study is to investigate whether personalized breast cancer

delivery is cost effective in medical practice. In this study, we investigated healthcare

delivery model for breast cancer patients to formulate an agile and adaptive

personalized alternative. In order to estimate costs and related lifetime gained per

patient, a personalized breast cancer treatment delivery model is built. In the model,

patients will get treatment according to the medical decisions based on the severity of

their cancer and their responses to the former treatment. Thus, it is possible to

calculate the costs of each individual’s treatment and to study the performance of this

type of healthcare delivery model. The proposed healthcare delivery model will

enable practitioners to address the following issues [35]:

Provide healthcare services through an integrated system that functions as

one unit

Don’t waste the patient’s time

Provide exactly what the patient needs

Provide what is needed, exactly when it is needed

Provide what is needed, when it is needed, exactly where it is needed

Provide aggregated services for quick response and low cost

Usually, there are several perspectives to assess the treatment model. For example,

payers, society and patients, contribute diverse perspectives on the problem. The

emphasis of the study is on the patients’ care, so a patients’ perspective is given

importance in this work.

Assumptions in the problem are as follows:

Patients are assumed to be disease-free when entering the system

The fixed costs and probabilities remain unchanged in the time horizon, but

a changeable range is set for parameters for further study

The other causes of death are not considered

18

The assumptions are made for the following reasons: The aim of personalized

medicine is to find the most tailored treatment strategies for the patient and her

personal preferences. With the restrictions of genetic technology and data

management, the patients will still receive treatment as subgroups. In real life,

patients may have more than one recurrence, each with a different degree of severity.

Suffering from multiple recurrences will greatly reduce the quality-adjusted life years

gained. It’s hard to predict the instances of recurrence, so one recurrence is assumed

for consistency. There certainly will be fluctuations in the costs due to factors like

medical care policies and improvement, but in this work, these fluctuations are

neglected.

3.2 Model building

Generally, the model consists of two parts. The first part is the breast cancer treatment

decision tree, in which patients go through each step of their medical care. Patients

are channeled through different branches based on the probabilities collected from

SEER (Surveillance, Epidemiology, and End Results Program). The costs will be

added up to account for the treatment they receive. The second part is the patients’

information. Patients’ data are stored according to age-based subgroups. A state chart

with patients’ disease stage is constructed. A Markov model is implemented. The state

chart will indicate whether the patients go into the follow up or have a recurrence and

go back to the system.

3.2.1 Population

According to the data from the National Cancer Institute, the number of new breast

cancer cases in 2014 is estimated to be 232,670, which does not include recurrence.

The incidence rate is calculated as follows:

𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒

= (𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑒𝑤 𝑐𝑎𝑛𝑐𝑒𝑟 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛⁄ 𝑠𝑖𝑧𝑒)

× 100,000

Based on the 2007 to 2011 cases, the average incidence rate of breast cancer among

women is 124.6 per 100,000. The US population is estimated at 318.9 million, and

19

female population is half of it. Although there are fluctuations in the number of new

breast cancer cases, the average entities entering the system would be 200,000 female

breast cancer cases per year.

There are also male patients with breast cancer. Compared with the high incidence

rate and diversity of the disease with female patients, the male cases are very few and

hence are not taken into consideration in the simulation.

Table 3.1 Age-adjusted SEER incidence rate of breast cancer from 2001 to 2011 [37]

Year of

Diagnosis

All Races

Male Female

2001 1.21 138.76

2002 1.15 135.73

2003 1.33 126.92

2004 1.21 128.03

2005 1.06 126.51

2006 1.19 126.14

2007 1.10 128.06

2008 1.17 128.11

2009 1.18 130.52

2010 1.24 126.46

2011 1.44 129.56

Age is a significant factor in the incidence of female breast cancer. Firstly, menopause

is highly related to the cause of breast cancer. The chance of getting breast cancer is

the highest in the range of 50 to 80 years of age. For elderly patients, age can also

affect the cause of the death of patients. Therefore, the patients are divided into age

groups when entering the system. In this case, patients are divided into five age

groups ranging from age 30 to 79.

20

Figure 3.1 SEER Incidence rates 2002-2011 [37]

Although race is also a criterion that can be used to group patients, it does not have a

major impact compared to age. Table 3.2 presents the incidence rate by races, age and

for years 2001 through 2011. There is no significant diversity within the same age

group of different races.

21

Table 3.2 Age-adjusted SEER incidence rate of breast cancer by race

For years 2001 through 2011 [37]

Year of

Diagnosis

All Races, Females White Females Black Females

All

ages

Ages

<50

Ages

50+

All

ages

Ages

<50

Ages

50+

All

ages

Ages

<50

Ages

50+

2001 138.76 43.96 387.03 144.95 44.84 407.09 115.41 39.77 313.48

2001 135.73 42.84 378.95 141.32 43.55 397.33 123.07 41.21 337.43

2003 126.93 43.33 345.8 131.79 44.28 360.95 123.49 43.24 333.63

2004 128.03 44.89 345.73 132.11 44.88 360.54 123.9 47.58 323.74

2005 126.51 44.05 342.44 131.43 44.51 359.03 118.23 45.61 308.41

2006 126.14 44.1 340.96 130.32 45.07 353.54 124.56 45.16 332.49

2007 128.06 45.19 345.05 132.33 45.72 359.13 124.07 44.96 331.22

2008 128.11 45.36 344.82 131.23 45.54 355.6 127.55 44.69 344.53

2009 130.52 45.31 353.64 134 45.81 364.93 128.4 45.28 346.04

2010 126.46 44.01 342.38 130.24 44.87 353.79 121.62 44.21 324.32

2011 129.56 45.23 350.39 132.58 45.07 361.73 126.69 48.07 332.57

3.2.2 Medical decision tree

The medical decision tree is the first step of the decision analysis. The paths of the

decision model represent how patients went through the system. The decision tree

basically answers the following questions:

Which treatment option should be chosen?

Will the patients receive the proceeding medical care? (Patients may be dead, or

they may not be suitable for continuing treatment anymore)

Is there still going to be a follow-up after the completion of the treatment?

The event nodes represent the specific treatment type. Those nodes are random event

nodes; the outcome node depends on the outcome of the condition of the previous

node. It’s a stochastic process. The branches represent the possible outcomes of the

event. The model’s attributes are used to split the nodes. Attributes are the disease

type (Ductal, Lobular, Tubular, Paget’s disease), tumor stage (early stage, invasive

22

breast cancer, metastasis), tumor size (<=0.5cm, 0.6-1cm, >1cm), ER Status (positive,

negative), PR Status (positive, negative), HER2 Status (positive, negative). The

probabilities decide which branch the entity will take.

There are two decision trees. The first one represents the traditional breast cancer

treatment process without the genetic information about the tumor (see Appendix A

for traditional breast cancer decision tree), while the second breast cancer decision

tree shows the personalized breast cancer treatment by adding prognostics gained

from biomarkers (see Appendix B for personalized breast cancer decision tree). Both

use the reference of national comprehensive cancer network guidelines. The model is

using the same population with the same symptoms undergoing different systems.

The decision flowchart works as follows. When a female patient enters the system,

first she will receive physical examinations and mammography in order to be roughly

classify her into category depending on the degree of breast cancer. In the three breast

cancer stages, which are divided according to the severity of the disease, this patient

will be further examined to receive proper treatment as she moves through the next

branch. The probabilities come from the SEER (Surveillance, Epidemiology, and End

Results Program) database of the National Cancer Institute. These medical decisions

are made based on the medical performance, genetic information, and personal

preferences.

3.2.3 Markov model

A Markov model can effectively track the development of patients’ stages as the

disease evolves over time. The different therapies will change the chance of relapse

differently. Patients will be in one of the five states: (1) patient is free of breast cancer;

(2) patient is in the early stage of breast cancer; (3) patient is diagnosed with

advanced stage breast cancer; (4) patient is in metastasis state; (5) patient is dead. The

model is formed in discrete times. The time interval between screening is yearly,

which means the patients’ states is checked at the end of each year. For the total time

23

horizon is 30 years.

In the Markov model, 𝑋𝑎 is a random event which occurs through out the time

horizon.

𝑋𝑎 =

{

01234

, for the corresponding states.

Figure 3.2 Prime state transitions for Markov model

Since it’s a stochastic process, the states should be mutually exclusive, which makes

the assumption that the probabilities of patients changing from one medical condition

to another are independent and do not affect each other. The assumptions are made as

follows:

The probability of the patient’s state is independent from the past states.

If the patient is free of breast cancer after the treatment, then she is assumed

to stay in that state to receive interval examination

The transition matrix is:

𝑃 =

𝑆𝑡𝑎𝑡𝑒 0 1 2 3 4 01234 [

𝑝00 𝑝01 𝑝02 𝑝03 𝑝04𝑝10𝑝20𝑝30𝑝40

𝑝11𝑝21𝑝31𝑝41

𝑝12𝑝22𝑝32𝑝42

𝑝13𝑝23𝑝33𝑝43

𝑝14𝑝24𝑝34𝑝44]

The fifth state is the absorbing state. 𝑝44 = 1 , Since patients cannot go back from a

24

more severe state to a less severe one, 𝑝𝑖𝑗 = 0 𝑓𝑜𝑟 𝑖 > 𝑗 > 0.

The model has the following Markov properties:

According to Chapman-Kolmogorov Equations and the discrete time property,

𝑝𝑖𝑗(𝑛) = ∑ 𝑝𝑖𝑘

(𝑚)𝑀𝑘=0 𝑝𝑘𝑗

(𝑛−𝑚),

The model for the Markov decision process is summarized below:

The state i of a discrete time Markov chain is observed after each transition

(i=0, 1, … , M)

After each observation, a medical decision is made to select an action from a

set of possible decisions on the medical decision tree.

If decision is made in state i, an immediate cost incurred with an expected

value and the life years gained will be changed right away. They will be

added to the statistics.

The decision in state i determines what the next stage of treatment is and

thus, transition probabilities will be determined for the next transition from

state i.

The objective is to find an optimal solution, which considers both costs and

life years gained that result from the future evolution of the process to either

minimize the cost or maximize the total life years gained. Afterwards,

calculate the associated cost effective results.

25

Chapter 4 Simulation Model

4.1 Modeling details

The logic of the discrete event simulation is explained in Figure 4.1:

Figure 4.1 The simulation logic

The simulation clock is set to zero. Patients enter the system at the rate of one per

minute. This model simulates both the traditional and the personalized breast cancer

treatment. After one replication, we get the statistics of the costs, life years gained,

and costs per life time gained. Every time we reset the control variables, a new

replication provides a new set of data. These recorded data are used for design of

experiments based analysis. The total replications are 27, since there are three factors

and each factor has three levels. The factors are examined to conclude their

26

relationships and outputs in the design of experiments.

There are two kinds of inputs to the model. Fixed parameters and input variables.

Fixed parameters are kept constant through the replication, while input variables are

changed from replication to replication as specified by the design of experiments. The

fixed parameters include fixed costs of traditional therapies, the frequencies of

probabilities, and the potential life years gained. All the fixed costs are listed in the

flowcharts. Regular costs are from the Healthcare Bluebook which are calculated as

averages serving as a fair fee for the payers. The frequencies are from SEER

(Surveillance, Epidemiology, and End Results Program). The SEER database is the

2013 SEER 18 regs research data almost covering the 27.8% of the US population.

The data of life years gained is from the online tools of Adjuvant! Online and breast

cancer conditional outcome calculator powered by CancerMath.net. Adjuvant! Online

is a validated decision making tool for predicting the chance of getting relapse as well

as the death rate. The therapy part of the Breast Cancer Treatment Outcome

Calculator is used to estimate the benefits of corresponding treatments.

27

Table 4.1 Fixed probabilities in the model

Parameter Estimate Reference

Early

stage

LCIS 18.38% SEER(2001-2003)

LCIS w/o cancer 0.06% SEER(2001-2003)

Localized only 49.33% SEER(2001-2003)

Lumpectomy Radiation therapy 0.09% journal JAMA Surgery+SEER

No radiation therapy 38.69% journal JAMA Surgery+SEER

Total mastectomy Sentinel node biopsy 24.13% journal JAMA Surgery+[p]

W/o Sentinel node biopsy 13.87% journal JAMA Surgery+[p]

Reconstruction 33.00%

2011 San Antonio Breast Cancer

Symposium

Invasive

breast

cancer

Regional by direct extension only 1.96% SEER(2001-2003)

Regional lymph nodes involved only 20.77% SEER(2001-2003)

Regional by both direct extension and lymph node

involvement 3.28% SEER(2001-2003)

Distant site(s)/node(s) involved 3.65% SEER(2001-2003)

Lumpectomy 48.12%

SEER(TNM)+journal JAMA

Surgery

Total mastectomy 29.50%

SEER(TNM)+journal JAMA

Surgery

Radiation chemotherapy 0.15% SEER(Radiaiton)

Radiation therapy 37.38% SEER(Radiaiton)

No radiation therapy 62.41% SEER(Radiaiton)

Conserving 22.38% SEER(TNM)

Desire preservation 6.28% SEER(surgery)

Not desire preservation 92.92% SEER(surgery)

ER+ 52.00% SEER(1990+)

ER- 13.67% SEER(1990+)

PR+ 43.46% SEER(1990+)

PR- 20.59% SEER(1990+)

HER2+ 10% [5.3.1]

HER2- 90% [5.3.1]

Tumor size <=0.5cm 5.89% SEER(TNM)

0.6-1.0cm 12.88% SEER(TNM)

>1cm 49.33% SEER(TNM)

Metastasis Bone disease 6.76% SEER AJCC M

*From 2013 SEER (Surveillance, Epidemiology, and End Results Program) 18 regs

research data

28

Table 4.2 Life years gained from different therapy choices

Age Tumor Size Therapy choice

hormonal chemo both

30-39

<=0.5cm 0.4 0.6 0.8

0.6-1.0cm 0.9 1.4 2.1

>1cm 1.3 1.9 2.8

40-49

<=0.5cm 0.2 0.3 0.5

0.6-1.0cm 0.6 0.9 1.3

>1cm 0.8 1.2 1.7

50-59

<=0.5cm 0.2 0.2 0.3

0.6-1.0cm 0.5 0.4 0.9

>1cm 0.7 0.6 1.2

60-69

<=0.5cm 0.1 0.1 0.2

0.6-1.0cm 0.3 0.2 0.5

>1cm 0.5 0.2 0.6

70-79

<=0.5cm 0.1 0 0.1

0.6-1.0cm 0.2 0.1 0.2

>1cm 0.2 0.1 0.3

*From Breast Cancer Conditional Outcome Calculator, Laboratory for Quantitative

Medicine LifeMath.net

The incidence rates and probabilities in the Markov model are collected from the

previous breast cancer studies, surveys and SEER database. Some decisions made by

patients are related to other factors. For example, younger patients (usually under 50)

and patients with private insurance after mastectomy tend to get breast reconstruction

immediately. Studies also show that younger patients are more likely to choose

mastectomy over lumpectomy.

29

Table 4.3 Probabilities in the Markov chain

Parameter Estimate(per year)

Age Disease free Local Regional Metastasis Death

Disease free

30-39 0.798 0.063 0.063 0.063 0.006

40-49 0.785 0.062 0.062 0.062 0.011

50-59 0.748 0.061 0.061 0.061 0.029

60-69 0.671 0.058 0.058 0.058 0.069

70-79 0.503 0.051 0.051 0.051 0.158

Local recurrence

30-39

0.941 0.0265 0.0265 0.04

40-49

0.936 0.0265 0.0265 0.039

50-59

0.919 0.026 0.026 0.039

60-69

0.88 0.026 0.026 0.037

70-79

0.792 0.025 0.025 0.032

Regional recurrecnce

30-39

0.941 0.053 0.04

40-49

0.936 0.053 0.039

50-59

0.919 0.052 0.039

60-69

0.88 0.052 0.037

70-79

0.792 0.05 0.032

Metastasis

30-39

0.941 0.04

40-49

0.936 0.039

50-59

0.919 0.039

60-69

0.88 0.037

70-79 0.792 0.032

*From Adjuvant!Online, Adjuvant! Inc.

The other kinds of input parameters are controllable and should be changed at each

replication. They are the focus of the study, including costs of genetic tests,

personalization percentage and the time interval between screening of the Markov

model. The base parameters of costs are first set from the literature. The cost of

21-gene assay is from the cost-effectiveness of 21-gene assay in node-positive, early

stage breast cancer [27]. The costs of endocrine therapy are from Lux et al. [30],

which also adopted the payer’s perspective under the adjuvant setting for

postmenopausal patients. The default time to check upon the patients’ status is one

year. In the current practice, a patient is followed up each year to see their condition

and chance of relapse. Therefore, the time interval between screening is set to one.

Here, the parameter personalization percentage is introduced to measure how much

30

the treatments are tailored to an individual patient. The personalization percent is

defined as the percentage of patients who will go through the new treatment process.

In the calculation of base case, it’s set to 0 and 100 as two extreme cases representing

two types of treatments.

Input data are categorized as parameters and variables in Anylogic. Parameters are

defined as the characteristics of the modeled objects, which have the same behavior

described in the class, but which differ in some parameter values. The parameters

include patients’ age, reconstruction decision, patients’ life years gained, cost per

patient, genetic costs, time interval between screening, patients’ survival rates, and

personalization percentage. Variables are generally used to store the results of the

simulation model or some characteristics of objects, and change over time. Variables

include patient number, cost altogether, average cost per patient, total life years

gained, average life years gained, and the number of deceased patients. Cost effective

ratio and cost per life years gained are set as statistic. The mean of these two

measuring outputs is studied.

The first replication is performed in the main part of the simulation. The results of the

first replication are used as the base case of the two scenarios. See the screenshots of

Anylogic (Appendix C). Java code (Appendix D) is written for the initialization of

entities’ attributes and their breast reconstruction decision.

4.2 Results

Currently, personalization is a trend in medical practice. There are six driving forces

in genetic services [33]: regulatory landscape, testing technology, reimbursement,

physician adoption, bio informatics, and consumer demand, which are making the

genetic services increase their adoption rate in clinical practice. There is no doubt that

there will be a higher personalization level. The targeted treatments based on genetic

evidence will reduce the number of tests required, improve the effectiveness, and

result in better outcomes for patients. In a not-too-far era, personalized medicine is

31

going to revolutionize the practice of medicine. It is necessary to formulate healthcare

delivery models for different health disciplines and get the potentially best model.

This in mind, we first get the results from the base case analysis.

In order to calculate the cost effectiveness ratio, prevalence rate should also be studied.

This study only considers female breast cancer. According to the following table,

1.3735% is used as the prevalence rate.

Table 4.4 SEER estimated prevalence percent on Jan 1st, 2011 in the previous 19

years

Race/

Ethnicity Sex All Ages 20-29 30-39 40-49 50-59 60-69 70-79

All Races

Both

Sexes 0.6995% 0.0061% 0.0850% 0.4724% 1.1895% 2.1765% 2.9663%

Males 0.0076% - 0.0005% 0.0027% 0.0085% 0.0257% 0.0464%

Females 1.3735% 0.0124% 0.1696% 0.9381% 2.3183% 4.1219% 5.3144%

White

Both

Sexes 0.7707% 0.0060% 0.0836% 0.4761% 1.2281% 2.3039% 3.1836%

Males 0.0083% - 0.0004% 0.0022% 0.0076% 0.0267% 0.0501%

Females 1.5296% 0.0123% 0.1710% 0.9647% 2.4332% 4.4294% 5.7661%

Black

Both

Sexes 0.4978% 0.0067% 0.0992% 0.4767% 1.0759% 1.8870% 2.5237%

Males 0.0072% - - 0.0051% 0.0155% 0.0339% 0.0414%

Females 0.9444% 0.0128% 0.1849% 0.8942% 1.9965% 3.3509% 4.2467%

Asian/Pacific

Iislander

Both

Sexes 0.5218% 0.0060% 0.0816% 0.4548% 1.0681% 1.6229% 2.0067%

Males 0.0043% - - 0.0020% 0.0074% 0.0109% 0.0263%

Females 0.9955% 0.0117% 0.1539% 0.8567% 1.9783% 2.9593% 3.5278%

Hispanio

Both

Sexes 0.2815% 0.0039% 0.0622% 0.3214% 0.8555% 1.5098% 1.9869%

Males 0.0020% - - 0.0013% 0.0042% 0.0144% 0.0238%

Females 0.5677% 0.0082% 0.1282% 0.6547% 1.6660% 2.7842% 3.4354%

Cost effective ratio is calculated in each case:

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑟𝑠𝑜𝑛𝑠 𝑐𝑜𝑣𝑒𝑟𝑒𝑑 × 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑝𝑒𝑟𝑠𝑜𝑛 𝑐𝑜𝑣𝑒𝑟𝑒𝑑

𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑟𝑠𝑜𝑛𝑠 𝑐𝑜𝑣𝑒𝑟𝑒𝑑 × 𝑃𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 × 𝑆𝑢𝑟𝑣𝑖𝑣𝑎𝑙 𝑟𝑎𝑡𝑒

=𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 𝑝𝑒𝑟𝑠𝑜𝑛 𝑐𝑜𝑣𝑒𝑟𝑒𝑑

𝑃𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 × 𝑆𝑢𝑟𝑣𝑖𝑣𝑎𝑙 𝑟𝑎𝑡𝑒

32

A replication is conducted first as the base case. In the base case of conventional

breast cancer treatment delivery system, average cost per patient is $87,463 and

average life years gained is 0.435. According to the statistics, cost effective ratio is

7,813,364 and the mean cost per life year gained for patients is $213,995. In the base

case of personalized system, average cost per patient is $44,355 and average life years

gained is 0.514. According to the statistics, cost effective ratio is 3,819,803 and the

mean cost per life year gained for patients is $67,114. It is obvious that the

personalized breast cancer treatment model is more cost effective because of the low

cost effectiveness ratio. For each life years gained, the patients spend less money and

gain more life years in the personalized treatment model.

4.3 Verification

The purpose of verification is to check whether the simulation correctly represents the

model. First, the flowchart constructed in Anylogic is the realization of the medical

decision making tree. Second, the input data is properly fed into the model. Three

major aspects are the costs, transition probabilities, and life years gained. The

outcomes are reasonable. No errors are reported at the end of each replication.

According to the statistics of SEER, the 5-year relative survival is 89%, which is

close to the simulation model output. In the end, the Anylogic debugger is used to

verify. Therefore, we can conclude that the simulation properly represents the model

itself.

4.4 Validation

The aim of validation is to see whether the model built embodies the true system. The

comparison can be made through the output data of base case simulation and data

from published articles. After 6 replications, we calculated the mean and standard

deviation of average cost per patient, average life years gained, cost per extra life time

gained and cost effective ratio. According to the review article [19], the costs of

treating breast cancer [19], the costs per patient, range from $20,000 to $100,000. In

both cases, average costs per patient fall into this range. Therefore, they are

33

reasonable figures. The output can be used for further study.

Table 4.5 Six replication of conventional base case

Replications Average cost

per patient

Average

life years

gained

Cost per extra

life year gained

Cost

effective

ratio

1 87463 0.435 213995 7813364

2 87455 0.435 213995 7816687

3 87457 0.435 214179 7817877

4 87463 0.436 213995 7816203

5 87467 0.435 214087 7820924

6 87488 0.436 214041 7815311

Sample mean 87465.5 0.435 214049 7816728

Standard deviation 11.862 0.001 73.468 2550.620

Table 4.6 Six replication of personalized base case

Replications Average cost

per patient

Average life

years gained

Cost per extra

life year gained

Cost

effective

ratio

1 44355 0.541 67114 3819803

2 44354 0.541 67114 3819492

3 44354 0.542 67205 3819456

4 44401 0.541 67198 3820986

5 44398 0.541 67134 3815435

6 44376 0.541 67083 3814562

Sample mean 44373 0.541 67141 3818289

Standard deviation 22.199 0.000 49.417 2623.393

34

Chapter 5 Design of Experiments

5.1 Control factors

Experiments according to the designs are performed to show how much benefit the

patients get and how much cost is incurred when we change the important factors in

the model. Researchers can therefore get the idea of which parameter has the most

influence on the results. A full factorial 3𝑘 design is used. Three factors are

considered; the three levels of each factor represent the low, intermediate and high

levels. In this study, the three factors are personalization percent, genetic tests costs,

and time interval between screening (represented by A, B, C in design of experiments

respectively). Personalization percent describes the involvement of personalization in

the healthcare delivery system. Genetic tests costs cover the costs of personalization.

Time interval between screening is taken as the substitute for the frequency to check

the relapse of patients. The objective of design of experiments in this study is

specified as follows:

Determine which parameters among personalization percent, genetic tests

costs and time interval between screening are the most influential to the

costs, life years gained, and cost effective ratio

Determine the value of parameters to set to obtain the lowest costs, most life

years gained and lowest cost effective ratio

Table 5.1 Factors and factor levels

Factors Label Factor Levels

1 Personalization Percent 0, 50 and 99

2 Genetic Tests Cost 1300, 4300 and 7300

3 Time Interval 1, 3 and 5

The model for the experiment is:

𝑌𝑖𝑗𝑘 = 𝜇 + 𝐴𝑖 + 𝐵𝑗 + 𝐴𝐵𝑖𝑗 + 𝐶𝑘 + 𝐴𝐶𝑖𝑘 + 𝐵𝐶𝑗𝑘 + 𝐴𝐵𝐶𝑖𝑗𝑘 + 𝜖𝑖𝑗𝑘

35

5.2 Full factorial 𝟑𝟑 design

For each scenario, there are three replications in the Minitab. The number of base runs

is 27, and with three replications there are 81 runs altogether. It’s a completely

randomized design. The randomized design table is shown in Appendix E. The

performance measures are average cost per patient, average life years gained, life

years gained per patient and cost effectiveness ratio. From the tables of analysis of

variance, we can decide which are the significant factors statistically. By observing

the main effects plots and the interaction plots, we can get the ranks of each factor

from the most important to the least important and determine, on average the best

setting for each factor. In a main effect plot, whether the line is horizontal decide

whether there exist main effects. The most important factor has the steepest line,

which means it has the biggest shift from one setting to another. For each setting, the

optimal solution can be identified based on the objective. In an interaction plot,

parallel lines indicate no interaction. However, it does not tell whether the interaction

is statistically significant.

36

Table 5.2 Output of average cost per patient

Run Personalization

percent

Genetic

test cost

Time

Interval Average cost per patient

1 1 1300 1 86458 86449 86439

2 1 1300 2 86465 86434 86437

3 1 1300 3 86451 86451 86430

4 1 4300 1 87033 87067 87047

5 1 4300 2 87042 87019 87008

6 1 4300 3 87046 86996 87035

7 1 7300 1 87611 87612 87609

8 1 7300 2 87608 87610 87632

9 1 7300 3 87586 87590 87606

10 50 1300 1 64157 64149 64157

11 50 1300 2 63704 63733 63735

12 50 1300 3 63303 63334 63307

13 50 4300 1 65901 65917 65909

14 50 4300 2 65484 65489 65477

15 50 4300 3 65026 65020 65016

16 50 7300 1 67648 67660 67695

17 50 7300 2 67227 67217 67215

18 50 7300 3 66776 66764 66753

19 99 1300 1 41835 41866 41858

20 99 1300 2 41010 41022 41024

21 99 1300 3 40195 40200 40194

22 99 4300 1 44790 44800 44785

23 99 4300 2 43912 43932 43916

24 99 4300 3 43049 43043 43038

25 99 7300 1 47728 47756 47732

26 99 7300 2 46805 46852 46820

27 99 7300 3 45918 46012 45920

37

Table 5.3 Analysis of Variance for Average Cost Per Patient, using Adjusted SS for

Tests

Source

Degrees of

Freedom

Sum of

Squares

Mean

Squares F p-value

A 2 25080278311 12540139156 43275020.1 0.000

B 2 164204746 82102373 283328.74 0.000

C 2 10321972 5160986 17810.15 0.000

A*B 4 48742333 12185583 42051.48 0.000

A*C 4 6628489 1657122 5718.6 0.000

B*C 4 12001 3000 10.35 0.000

A*B*C 8 7436 930 3.21 0.005

Error 54 15648 290

total 80 25310210936

Table 6.2 and Table 6.3 are the summary of design of experiments for the output of

average cost per patient. In Minitab, when p value is less than 0.001, is reported as

0.000. It can be concluded that the interaction A*B*C is not a significant factor. All

of the three factors have a significant effect on the average costs per patient.

Figure 5.1 Normal plot of Residuals for Average Cost Per Patient

As we can see from the residuals plot, the fluctuation in the system is pretty stable. It

basically follows the normal assumption. Since the 54th observation highly out of

position, it is excluded from the data set.

38

Figure 5.2 Main effects plot matrix for Average Cost Per Patient

Figure 5.3 Interaction plot matrix for Average Cost Per Patient

We can conclude from main effects plots that higher personalization percent can

actually lower the average cost per patient when the genetic costs is constant. The

most important factor is the personalization percent. When the personalization percent

is low, the other two factors barely have any influence over the output. The time

interval between screening and genetic tests costs both have only a small effect on the

output. It’s obvious that the average costs go up as the genetic tests costs rise. When

the patients are checked less frequently for the recurrence, the average costs can be

slightly lowered. In order to make the average cost per patient lower, the ideal setting

should have higher personalization percent, lower genetic costs, and longer time

39

interval between screening. In the interaction plot matrix, the lines are relatively

parallel, so the interactions are not evident, and the three factors are not dependent on

each other.

Table 5.4 Output of average life years gained

Run

Personalization

percent

Genetic

test cost

Time

Interval Average life years gained

1 1 1300 1 0.436 0.436 0.436

2 1 1300 2 0.437 0.437 0.436

3 1 1300 3 0.436 0.436 0.436

4 1 4300 1 0.437 0.437 0.436

5 1 4300 2 0.437 0.436 0.436

6 1 4300 3 0.436 0.437 0.436

7 1 7300 1 0.437 0.436 0.437

8 1 7300 2 0.437 0.436 0.437

9 1 7300 3 0.436 0.436 0.436

10 50 1300 1 0.488 0.488 0.488

11 50 1300 2 0.485 0.485 0.485

12 50 1300 3 0.481 0.481 0.482

13 50 4300 1 0.488 0.488 0.488

14 50 4300 2 0.485 0.486 0.485

15 50 4300 3 0.481 0.482 0.481

16 50 7300 1 0.488 0.488 0.488

17 50 7300 2 0.485 0.485 0.485

18 50 7300 3 0.482 0.481 0.481

19 99 1300 1 0.54 0.54 0.54

20 99 1300 2 0.533 0.533 0.534

21 99 1300 3 0.526 0.526 0.526

22 99 4300 1 0.54 0.54 0.54

23 99 4300 2 0.533 0.533 0.533

24 99 4300 3 0.526 0.527 0.527

25 99 7300 1 0.54 0.54 0.541

26 99 7300 2 0.533 0.533 0.533

27 99 7300 3 0.527 0.527 0.526

40

Table 5.5 Analysis of Variance for Average Life Years Gained, using Adjusted SS

for Tests

Source

Degrees of

Freedom

Sum of

Squares

Mean

Squares F p-value

A 2 0.1266338 0.0633169 366333.5 0.000

B 2 0.0000004 0.0000002 1.14 0.326

C 2 0.0006413 0.0003206 1855.14 0.000

A*B 4 0.0000002 0 0.29 0.886

A*C 4 0.0004015 0.0001004 580.79 0.000

B*C 4 0.0000007 0.0000002 1.04 0.397

A*B*C 8 0.0000016 0.0000002 1.14 0.351

Error 54 0.0000093 0.0000002

total 80 0.1276888

Tables 6.4 and 6.5 are the summary of design of experiments for the output of average

life years gained per patient. It can be concluded that factors A and C, and the

interaction A*C are all significant. All of the three terms have a significant effect on

the average costs per patient; their p values are smaller than 0.001.

Figure 5.4 Normal plot of Residuals for Average Life Years Gained

As we can see from the residuals plot, the residuals are really small and can almost be

ignored. The observations are normal and should be retained.

41

Figure 5.5 Main effects plot matrix for Average Life Years Gained

Figure 5.6 Interaction plot matrix for Average Life Years Gained

We can conclude from both the plots that higher personalization percent can increase

the average life years per patient. The genetic tests costs don’t have any impact in

this output. When patients are checked more frequently, they will gain more life years

during the treatment. In order to make the average life years higher, the ideal setting

should have higher personalization percent, and shorter time interval between

screening.

42

Table 5.6 Output of average cost per patient

Run

Personalization

percent

Genetic

test cost

Time

Interval Cost per extra life year gained

1 1 1300 1 209556 209644 209556

2 1 1300 2 209424 209424 209424

3 1 1300 3 209644 209468 209556

4 1 4300 1 209424 209205 209600

5 1 4300 2 209424 209512 209556

6 1 4300 3 209512 209424 209556

7 1 7300 1 209293 209600 209424

8 1 7300 2 209424 209556 209336

9 1 7300 3 209556 209600 209512

10 50 1300 1 102114 102082 102135

11 50 1300 2 102082 102135 102114

12 50 1300 3 102114 102124 102030

13 50 4300 1 102072 102187 102135

14 50 4300 2 102124 102020 102145

15 50 4300 3 102114 101958 102082

16 50 7300 1 102072 102114 102176

17 50 7300 2 102124 102062 102062

18 50 7300 3 102062 102155 102072

19 99 1300 1 67613 67568 67522

20 99 1300 2 67249 67295 67249

21 99 1300 3 66979 66979 66979

22 99 4300 1 67613 67568 67568

23 99 4300 2 67295 67295 67295

24 99 4300 3 66979 66934 66954

25 99 7300 1 67567 67589 67542

26 99 7300 2 67249 67200 67231

27 99 7300 3 66979 66940 66938

43

Table 5.7 Analysis of Variance for Cost Per Extra Life Year Gained, using Adjusted

SS for Tests

Source

Degrees of

Freedom

Sum of

Squares

Mean

Squares F p-value

A 2 2.96763E+11 1.48381E+11 31490117.5 0.000

B 2 8191 4095 0.87 0.425

C 2 535102 267551 56.78 0.000

A*B 4 11293 2823 0.6 0.665

A*C 4 1177819 294455 62.49 0.000

B*C 4 23049 5762 1.22 0.312

A*B*C 8 40631 5079 1.08 0.392

Error 54 254448 4712

total 80 2.96765E+11

Table 6.6 and Table 6.7 are the summary of design of experiments for the output of

costs per extra life year gained. It can be concluded that factor A, C and the

interaction A*C are all significant factors.

Figure 5.7 Normal plot of Residuals for Cost Per Extra Life Year Gained

As we can see from the residuals plot, there are five points have the largest residuals

because of the system fluctuations.

44

Figure 5.8 Main effects plot matrix for Cost Per Extra Life Year Gained

Figure 5.9 Interaction plot matrix for Cost Per Extra Life Year Gained

We can conclude from both the plots that higher personalization percent can actually

lower the cost per life time gained no matter how the other two factors change. As the

personalization percent goes higher, the cost per life time gained has the steeper line

in the change from 1 to 50. The most important factor is the personalization percent.

The time interval between screening and genetic tests costs both have almost no

effects on the output. It is obvious that the average costs go up as the genetic costs

rise. In order to get the lower cost per life year gained, the ideal setting should have

higher personalization percent.

45

Table 5.8 Output of average cost per patient

Run

Personalization

percent

Genetic

test cost

Time

Interval Cost effective ratio

1 1 1300 1 7724060 7722469 7724078

2 1 1300 2 7728406 7721638 7721709

3 1 1300 3 7723647 7722959 7717711

4 1 4300 1 7780060 7781645 7777949

5 1 4300 2 7779065 7768299 7772718

6 1 4300 3 7771562 7771500 7770658

7 1 7300 1 7826790 7826675 7826407

8 1 7300 2 7827763 7828582 7828947

9 1 7300 3 7824312 7823283 7828305

10 50 1300 1 5795348 5794636 5680385

11 50 1300 2 5747308 5585822 5750105

12 50 1300 3 5510757 5509557 5711492

13 50 4300 1 5831396 5833338 5946241

14 50 4300 2 5907898 5731865 5734136

15 50 4300 3 5866578 5651205 5654845

16 50 7300 1 5985393 5984676 6114950

17 50 7300 2 6065150 6064248 5882804

18 50 7300 3 5802955 5802629 5801458

19 99 1300 1 3601738 3603596 3603139

20 99 1300 2 3420093 3419301 3419712

21 99 1300 3 3278546 3278087 3277902

22 99 4300 1 3860045 3860530 4086025

23 99 4300 2 4001361 3666749 3667275

24 99 4300 3 3518634 3516150 3510782

25 99 7300 1 4117832 4117892 4118235

26 99 7300 2 3913514 3912490 3913908

27 99 7300 3 3757363 3758012 2757460

46

Table 5.9 Analysis of Variance for Cost Effective Ratio, using Adjusted SS for Tests

Source

Degrees of

Freedom

Sum of

Squares

Mean

Squares F p-value

A 2 2.28154E+14 1.14077E+14 6682.2 0.000

B 2 8.81483E+11 4.40742E+11 25.82 0.000

C 2 6.86479E+11 3.4324E+11 20.11 0.000

A*B 4 2.42477E+11 60619213202 3.55 0.012

A*C 4 5.29051E+11 1.32263E+11 7.75 0.000

B*C 4 68882649535 17220662384 1.01 0.411

A*B*C 8 81927926114 10240990764 0.6 0.774

Error 54 9.21874E+11 17071735113

total 80 2.31566E+14

Tables 6.8 and 6.9 are the summary of design of experiments for the output of cost

effective ratio. Except for A*C all the other interactions are not significant. We can

also conclude that personalization rate, genetic costs and time interval between

screening all have significant impact on the cost effective ratio.

Figure 5.10 Normal plot of Residuals for Cost Effective Ratio

As we can see from the residuals plot, observation 81 should be excluded from the

sample.

47

Figure 5.11 Main effects plot matrix for Cost Effective Ratio

Figure 5.12 Interaction plot matrix for Cost Effective Ratio

We can conclude from both the plots that all the three factors have influence on the

cost effectiveness ratio. Higher personalization percent can decrease the cost

effectiveness ratio. The most important factor is the personalization percent. When the

personalization percent is low, the other two factors still barely have any influence

upon the output. Neither the time interval between screening nor the genetic tests

costs have much significant effects on the output. The cost effective ratio goes up as

the genetic costs rise. When the patients are checked less frequently for recurrence,

the cost effectiveness ratio can be slightly lowered. The lower the cost effectiveness

ratio is, the higher effectiveness the treatment is. Therefore, the ideal setting should

48

have higher personalization percent, lower genetic costs and a longer time interval

between screening.

49

Chapter 6 Results and conclusions

An agile and adaptive personalized healthcare delivery model for breast cancer

patients has the potential to revolutionize medical care by utilizing improved

understanding of cancer delivery methods, research and technology to allow for better

diagnostic and treatment options, reduced cancer treatment costs, greater

predictability of disease course, and improved patient safety by selecting not only the

right drug for a patient but also the proper dosage to reduce adverse effects.

In this research, we investigated and demonstrated an agile and adaptive personalized

healthcare delivery model by: (1) adopting proven strategies and concepts from

product/service mass customization to health care; (2) bringing developments in the

personalized medicine field to agenda; (3) capturing the latest innovative advances in

the field of medical devices, health monitoring devices, diagnosis, cure, therapy, and

behavioral intervention; (4) supporting the use of electronic medical record,

information technology and other computational tools to create a more automated

system in healthcare; (6) developing unique business and service models that improve

patient care, costs and incentives.

According to the study results, we conclude that a better personalization of breast

cancer delivery is increasing the personalization percent, decreasing the genetic tests

costs, and prolonging the time interval between screening to check the recurrence.

The contributions of the work offer valuable benefits to both academia and health care

providers. Researchers and pioneers of personalized medicine will have the

opportunity to explain the meaning and revolutionary aspects of personalized

medicine to the public. Healthcare providers will gain a better understanding of

personalized medicine and the significance of predictive, preventive and curative

medicine. Moreover, the outcomes of proposed work are equally applicable to all

types of cancers, such as lung cancer, colon cancer, prostate cancer and others.

50

REFERENCES

1. Beatson GT. On the treatment of inoperable cases of carcinoma of the Mamma:

suggestions for a new method of treatment, with illustrative cases. CA-A

Cancer Journal for Clinicians, 1983; 33: 108-121.

2. Russell CA. Personalized medicine for breast cancer: it is a new day. The

American Journal of Surgery, 2014; 207: 321-325.

3. Olopade OL, Grushko TA, Nanda R, et al. Advances in Breast Cancer:

Pathways to Personalized Medicine. Clinical Cancer Research, 2008; 14:

7988-7999.

4. Ellis MJ. Mutational analysis of breast cancer: Guiding personalized

treatments. The Breast, 2013; 22: 19-21.

5. Harbeck N, Salem M, Nitz U, Gluz O, Liedtke C. Personalized treatment of

early-stage breast cancer: Preset concepts and future directions. Cancer

Treatment Reviews, 2010; 36: 584-594.

6. Harbeck N, Sotlar K, Wuerstlein R, Doisneau-Sixou S. Molecular and protein

markets for clinical decision making in breast cancer: Today and tomorrow.

Cancer Treatment Reviews, 2014; 40: 434-444.

7. Chen T, Bedard PL. Personalized medicine for metastatic breast cancer.

www.co-oncology.com, 2013; 25: 615-624.

8. Drier Y, Sheffer M, Domany E. Pathway-based personalized analysis of cancer.

PNAS, 2013; 110: 6388-6393.

51

9. Rivenbark AG, O’Connor SM, Coleman WB. Molecular and Cellular

Heterogeneity in Breast Cancer: Challenger for Personalized Medicine. The

American Journal of Pathology, 2013; 183: 1113-1124.

10. Coleman WB. Breast cancer Personalized medicine: Challenges and

Opportunities. The American Journal of Pathology, 3013; 183: 1036-1037.

11. Weldon CB, Trosman JR, Gradishar WJ, Benson A, Schink JC. Barriers to the

Use of Personalized Medicine in Breast Cancer. Journal of Oncology Practice,

2012; 8: 24-31.

12. Gail MH. Personalized estimates of breast cancer risk in clinical practice and

public health. Statistics in Medicine, 2011; 30: 1090-1104.

13. Ko MG, Files JA, Pruthi S, et al. Reducing the risk of breast cancer: A

personalized approach. The Journal of Family Practice, 2012; 61: 340-347.

14. Laura D. Scherer, Peter A Ubel, Jennifer McClure, Sarah M. Greene, Sharon

Hensley Alford, Lisa Holtzman, Nicole Exe, Angela Fagerlin. Belief in

numbers: When and why women disbelieve tailored breast cancer risk

statistics. Patient Education and Counseling, Vol. 92, pp. 253-259, 2013.

15. Ozanne EM, Howe R, Omer Z, Esserman LJ. Development of a personalized

decision aid for breast cancer risk reduction and management. BMC Medical

Information and Decision Making, 2014; 14: 1-8.

16. Mandelblatt JS, Stout N, Trentham AD. To Screen or Not to Screen Women in

Their 40s for Breast Cancer: Is Personalized Risk-Based Screening the Answer.

Annals of Internal Medicine, 2011; 155:58-60.

17. Inaji H. Management of breast cancer: from standardization to personalization.

Breast Cancer, 2009; 16: 239-240.

18. Sabatier R, Goncalves A, Bertucci F. Personalized medicine: Present and

future of breast cancer management. Oncology Hematology. 2014; 1846:1-11.

19. Campbell JD, Ramsey SD. The costs of treating breast cancer in the US, A

synthesis of published evidence. Pharmacoeconomics, 2009; 27: 199-209.

20. Johnston K. Modelling the future costs of breast screening. European Journal

of Cancer, 2001; 37: 1752-1758.

52

21. Lauzier S, Maunsell E, Drolet M, Coyle D, He ́bert N. Validity of information

obtained from a method for estimating cancer costs from the perspective of

patients and caregivers. Qual Life Res, 2010; 19: 177–189.

22. Carter KJ, Ritchey NP, Castro F, Caccamo LP, Edward Kessler and Barbara A.

Erickson. Analysis of Three Decision-making Methods: A Breast Cancer

Patient as a Model. Med Decis Making, 1999; 10: 19-49.

23. Jerez J, Gomez J, Ramos G, Munoz J, Alba E. A combines neural network and

decision trees model for prognosis of breast cancer relapse. Artificial

Intelligence in Medicine, 2013; 27: 45-63.

24. Chan KJ. A semi Markov Model for Mammographic Detection of Breast

Cancer. Master thesis Carleton University, 1996

25. Griffiths A. Cost-effectiveness and cost-benefit analysis of health services: the

methodology and its application. Health Policy, 1988; 9: 251-165.

26. Chang JC, Chen TH, Duffy SW, et al. Decision Modeling of economic

evaluation of intervention programme of breast cancer. Journal of Evaluation

in Clinical Practice, 2010; 16: 1282-1288.

27. Vanderlaan BF, Broder MS, Chang EY, Oratz R, Bentley T. Cost-effectiveness

of 21-Gene Assay in Node-Positive, Early-Stage Breast Cancer. The American

Journal of Managed Care. 2011; 17: 455-464.

28. Yang M, Rajan S, Issa AM, Cost effectiveness of Gene Expression Profiling

for Early Stage Breast Cancer, A Decision-Analytic Model. Wiley Online

Library, Cancer, 2012: 5163-5170.

29. Chubiz JE, Lee JM, Gilmore ME, Kong CY, et al. Cost-effectiveness of

Alternating Magnetic Resonance Imaging and Digital Mammography

Screening in BRCA1 and BRCA2 Gene Mutation Carriers. Wiley Online

Library, Cancer, 2013: 1266-1276.

30. Lux MP, Reichelt C, Kainon J, Tanzer TD, Radosavac D, Fasching PA,

Beckmann MW, Thiel FC. Cost-Benefit Analysis of Endocrine Therapy in the

Adjuvant Setting for Postmenopausal Patients with Hormone

Receptor-Positive Breast Cancer, Based on Survival Data and Future Prices

53

for Generic Drugs in the Context of the German Health Care System. Breast

Care, 2011; 6: 381-389.

31. Oltra A, A. Munarriz SB, Pastor M, Montalar J. Cost-Benefit Analysis of a

Follow-up Program in Patients with Breast Cancer: A Randomized Prospective

Study. The Breast Journal, 2001; 13: 571-574.

32. Verry H, Lord SJ, Martin A, Gill G et al. Effectiveness and cost-effectiveness

of sentinel lymph node biopsy compared with axillary node dissection in

patients with early-stage breast cancer: a decision model analysis. British

Journal of Cancer, 2012; 106: 1045-1052.

33. Leslie T, Agar D, Fielding S, Miller S. Market trends in genetic services:

Impacting Clinical care through better prediction, detection and care selection.

Booz Allen Hamilton Inc. 2013: 1-12.

34. Cancer facts and figures 2014, American Cancer Society Inc. 2014: 1-67.

35. American Cancer Society recommendations for early breast cancer detection

in women without breast symptoms, American Cancer Society Inc.

36. Breast clinical care pathway. Breast Cancer Program, Public Health

Programmes, Health Authority, Abu Dhabi. 2009

37. Surveillance, Epidemiology, and End Results Proogram. seer.cancer.gov/.

2014.

54

Appendix A

List of Abbreviations

BRCA

CT

CTCs

ER

HER2

LN

NGS

QUALYs

TNBC

Breast cancer gene

Computed tomography

Circulating tumor cells

Estrogen receptor

Human epidermal growth receptor-2

Lymph node

Next generation sequencing

Quality adjusted life years

Triple negative breast cancer

55

Appendix B

Medical Decision Trees

Figure B1 Traditional breast cancer decision tree (continued)

56

Figure B1 Traditional breast cancer decision tree

57

Figure B2 Personalized breast cancer decision tree (continued)

58

Figure B2 Personalized breast cancer decision tree (continued)

59

Figure B2 Personalized breast cancer decision tree (continued)

60

Figure B2 Personalized breast cancer decision tree

61

Appendix C

Anylogic Screen Shots

62

Figure C Anylogic screenshots

63

Appendix D

Java Code for Patients Parameters Initialization

Entity Group: Patient

public class Patient extends com.xj.anylogic.libraries.enterprise.Entity implements

java.io.Serializable {

int Age = 0;

double LY = 0;

double QALY = 0;

double hospitalStay = 0;

double totalCost = 0;

boolean reconstructionDecision;

double timeBeforeRecurrecne = 0 ;

public double tumorSize = 0;

public Patient(){

if(Math.random()<=0.0132){

Age=35;

} else if(Math.random()<=0.0861){

Age=45;

} else if(Math.random()<=0.2663){

Age=55;

} else if(Math.random()<=0.5868){

Age=65;

} else {

Age=75;

}

if (Math.random() <= 0.63){

reconstructionDecision = true;

}

else {

reconstructionDecision = false;

}

}

public Patient(int Age, double LY, double QALY, double hospitalStay, double

totalCost, boolean reconstructionDecision,

double timeBeforeRecurrecne, double tumorSize){

this.Age = Age;

this.LY = LY;

this.QALY = QALY;

this.hospitalStay = hospitalStay;

this.totalCost = totalCost;

this.reconstructionDecision = reconstructionDecision;

64

this.timeBeforeRecurrecne = timeBeforeRecurrecne;

this.tumorSize = tumorSize;

}

@Override

public String toString() {

return

"Age = " + Age +" " +

"LY = " + LY +" " +

"QALY = " + QALY +" " +

"hospitalStay = " + hospitalStay +" " +

"totalCost = " + totalCost +" " +

"reconstructionDecision = " + reconstructionDecision +" " +

"timeBeforeRecurrecne = " + timeBeforeRecurrecne +" "+

"tumorSize = " + tumorSize +" ";

}

private static final long serialVersionUID = 1L;

}

65

Appendix E

Design of Experiments

Run Blk A B C

1 1 2 2 3

2 1 3 3 3

3 1 1 2 3

4 1 2 1 1

5 1 1 3 2

6 1 1 1 2

7 1 3 3 1

8 1 2 2 2

9 1 1 2 1

10 1 3 2 3

11 1 1 2 2

12 1 2 3 3

13 1 3 2 1

14 1 3 2 2

15 1 1 1 3

16 1 2 2 2

17 1 3 1 3

18 1 1 3 3

19 1 2 3 1

20 1 2 1 2

21 1 3 1 1

22 1 1 1 1

23 1 1 3 2

24 1 2 3 1

25 1 1 3 3

26 1 3 3 3

27 1 3 1 3

28 1 2 3 2

29 1 3 3 3

30 1 1 2 2

31 1 1 3 1

32 1 3 2 1

33 1 3 3 1

34 1 1 2 1

35 1 2 3 1

36 1 3 2 2

37 1 2 2 1

38 1 2 1 3

39 1 1 1 2

40 1 2 1 1

41 1 1 2 1

42 1 2 3 2

43 1 3 2 2

44 1 3 3 2

45 1 2 2 1

46 1 1 3 1

47 1 3 1 2

48 1 3 2 3

49 1 1 1 3

50 1 2 1 3

51 1 3 1 3

52 1 1 3 3

53 1 2 2 1

54 1 2 2 2

55 1 2 1 2

56 1 3 3 2

57 1 3 2 3

58 1 2 3 3

59 1 2 1 2

60 1 1 2 3

61 1 3 1 1

62 1 1 2 3

63 1 1 3 1

64 1 1 1 2

65 1 3 2 1

66 1 1 1 1

67 1 3 1 2

68 1 2 2 3

69 1 1 1 3

70 1 2 1 3

71 1 3 3 2

72 1 1 1 1

73 1 3 1 2

74 1 2 3 2

75 1 2 3 3

76 1 1 3 2

77 1 3 3 1

78 1 1 2 2

79 1 2 2 3

80 1 2 1 1

81 1 3 1 1