Statistical Methods in Micro-Simulation Modeling: Calibration and Predictive Accuracy

202 Pages·2013·3.85 MB·English

Statistical Methods in
Micro-Simulation Modeling:
Calibration and Predictive
Accuracy
by
Stavroula Chrysanthopoulou
B.S., Athens University of Economics and Business, 2003
Sc. M., University of Athens, 2007
A Dissertation submitted in partial fulfillment of the
requirements for the Degree of Doctor of Philosophy
in Biostatistics, at Brown university
Providence, Rhode Island
May 2014
(cid:13)c Copyright 2014 by Stavroula Chrysanthopoulou
This dissertation by Stavroula Chrysanthopoulou is accepted in its present form
by the SPH department of Biostatistics as satisfying the
dissertation requirement for the degree of Doctor of Philosophy.
Date
Constantine Gatsonis, PhD (Advisor)
Recommended to the Graduate Council
Date
Carolyn Rutter, Reader, PhD (Reader)
Date
Xi Luo, PhD (Reader)
Date
Matthew Harrison, PhD (Reader)
Approved by the Graduate Council
Date
Peter Weber, Dean of the Graduate School
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Curriculum Vitæ
Stavroula Chrysanthopoulou was born on May 2, 1980, in Athens, Greece.
She received her BSc degree in Statistics from Athens University of Economics and
Business (AUEB), in September 2003, and her MSc degree in Biostatistics from
University of Athens (UOA), in February 2007.
In September 2008 she was admitted to the PhD program in Biostatistics, at Brown
University, from where she received her second MSc degree in Biostatistics in 2010.
She successfully defended her PhD Dissertation entitled ”Statistical Methods in
Micro-Simulation Modeling: Calibration and Predictive Accuracy”, on September
13, 2013.
During her five years career as a PhD candidate, she was appointed as a teaching
assistantinthefollowingcourses, offeredbythedepartmentofBiostatisticsatBrown
University:
• Introduction to Biostatistics (Fall semester, 2008)
• Applied Regression Models (Spring semester, 2009)
• Analysis of Life Time Data (Spring semester, 2012)
She presented a poster entitled ”Relationship between breast biopsies and family
histrory of breast cancer”, at the Brown University Public Health Research Day, in
Spring semester 2010.
She also presented part of her dissertation work as an invited speaker in the ”Micro-
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simulation Models for Health Policy: Advances and Applications” session, at the
Joint Statistical Meetings (JSM) 2013 conference in Montreal, Canada.
She has several years of working experience as:
⇒ 2003-2005: Consulting Biostatistician, mainly involved in the design and con-
duct of statistical analysis for biomedical papers.
⇒ 2005-2008: Statistical Consultant at Agilis SA-Statistics and Informatics, in-
volved with research on methods for official statistics in projects conducted by
the European Statistical Service (Eurostat)
Her research interests are focused on statistical methods for complex predictive mod-
els, such as Micro-simulation Models (MSMs) used in medical decision making, as
well as on High Performance Computing (HPC) techniques for complex statistical
computations using the open source statistical package R.
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Acknowledgements
The five years of my life as a PhD candidate were full of valuable experiences, ex-
ceptional opportunities to improve myself both as a scientist and as a human being,
and of course a lot of challenging moments. In this beautiful “journey” I was blessed
by God to be surrounded by very important people, without the support of whom I
would never be able to achieve my goal.
First and foremost I would like to thank my advisor, Professor Constantine Gatsonis,
for his willingness to work with me in this very interesting field, and his continuing
support and guidance that helped me to overcome all the obstacles and conduct this
important research. His intelligence, ethos, and integrity render him the perfect role
model for young scientists. I want to also express my gratitude to Dr Carolyn Rutter
for her valuable feedback as an expert in micro-simulation modeling, as well as for
theexceptionalopportunitiessheprovidedmewithtopresentmyworkandexchange
opinions with experts in the field. I would also like to thank Dr Matthew Harrison
for his felicitous comments and insight that helped me to improve the Empirical
calibration method, as well as to better organize and carry out the daunting task
of calibrating a micro-simulation model. Thanks also to Dr Xi Luo for serving as a
reader in my thesis committee.
I am also grateful to people from the Brown Center for Computation and Visual-
ization support group, especially Mark Howison and Aaron Shen for always being
very responsive and effective in helping me with the implementation of exhaustive
parallel processes in R. I also thank Dr Samir Soneji for his assistance in estimating
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Cumulative Incidence Functions from the National Health Interview Survey data.
I also thank all the faculty, staff, and students of the Brown School of Public Health.
Especially I want to thank all my professors from the Biostatistics department, the
staff of the Center of Statistical Sciences (CSS), and my classmates. Special thanks
go to Denise Arver and Elizabeth Clark for always being very responsive and con-
siderate.
Besides the people in the Academic environment, I was also blessed to have a beau-
tiful family and some wonderful friends that were always there for me in all the ups
and downs of my career as a PhD candidate. To all these people I owe a great deal
of my achievement.
I have no words to express how blessed I am for growing up in a very loving and
caring family who always believed in and supported me. I want to thank my father
for the first nine, full of love years of my life, as well as for being my good angel since
the day he passed away. There is no way to thank my wonderful mother enough, for
dedicating her life to my brother and me, and holding very successfully both parental
roles the past twenty four years of my life. She has been without exaggeration the
best mother ever! I owe her all the good (if any) elements of my personality and a
large portion of the success in my life until now. For all these reasons I will always
be very grateful and proud of being her daughter.
I would also like to thank my brother Vassilios, for always being a good example
for me and undertaking a large portion of the burden as the protector of our family
after the loss of our father. I am also grateful to my brother’s family, his wife Ioanna
Andreopoulou, who I consider a true sister, and my two little “Princesses” Katerina
and Antonia, for the positive effect they have on me.
God has indeed been very generous with me by sending invaluable friends in my life.
IwouldfirstliketothankDrJessicaJalbert, DrDhirajCatoorandDrSinanKaraveli
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for considerably helping me with my installation here in Providence. Special thanks
also go to the Perdikakis family, the parents Ann and Costas, and the children Rhea
and Damon Ray, Giana, and Dean for their support, caring and love. I am very
grateful for meeting and being part of this amazing family.
Last but not least I would like to express my gratitude to my heart friend Nektaria
for the continuing support, her kindness and thoughtfulness, and most importantly
for the great honor she did me to baptize her first born, Anna.
Unfortunately, due to space constraints, I have to finalize my list by thanking from
the bottom of my heart all the aforementioned people as well as many other valuable
friends, relatives and important persons in my life. Truly and deeply thankful for
their positive effect in my life, I dedicate my accomplishment to them all.
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Abstract of “Statistical Methods in Micro-Simulation Modeling:
Calibration and Predictive Accuracy”
by Stavroula Chrysanthopoulou, Ph.D., Brown University, May 2014
This thesis presents research on statistical methods for the development and evalu-
ation of micro-simulation models (MSM). We developed a streamlined, continuous
time MSM that describes the natural history of lung cancer, and used it as a tool for
theimplementationandcomparisonofmethodsforcalibrationandassessmentofpre-
dictive accuracy. We performed a comparative analysis of two calibration methods.
The first employs Bayesian reasoning to incorporate prior beliefs on model parame-
ters, and information from various sources about lung cancer, to derive posterior dis-
tributions for the calibrated parameters. The second is an Empirical method, which
combines searches of the multi-dimensional parameter space using Latin Hypercube
SamplingdesignwithGoodnessofFitmeasurestospecifyparametervaluesthatpro-
vide good fit to observed data. Furthermore, we studied the ability of the MSMs to
predict times to events, and suggested metrics, based on concordance statistics and
hypothesis tests for survival data. We conducted a simulation study to compare the
performance of MSMs in terms of their predictive accuracy. The entire methodology
was implemented in R.3.0.1. Development of an MSM in an open source statistical
software enhances the transparency, and facilitates research on the statistical prop-
erties of the model. Due to MSMs complexity, use of High Performance Computing
techniques in R is essential to their implementation. The analysis of the two cali-
bration methods showed that they result in extensively overlapping set of values for
the calibrated MSM parameters, and MSM outputs. However, the Bayesian method
performs better in the prediction of rare events, while the Empirical method proved
more efficient in terms of the computational burden. The assessment of predictive
accuracy showed that among the methods suggested here, hypothesis tests outper-
form concordance statistics, since they proved more sensitive for detecting differences
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between predictions, obtained by the MSM, and actual individual level data.
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