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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
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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
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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
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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
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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
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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;
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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