Confounding Adjustment in Comparative Studies of Newly Marketed Medications The Harvard ...

105 Pages·2015·1.67 MB·English

Confounding Adjustment in Comparative Studies of
Newly Marketed Medications
Citation
Kumamaru, Hiraku. 2015. Confounding Adjustment in Comparative Studies of Newly Marketed
Medications. Doctoral dissertation, Harvard T.H. Chan School of Public Health.
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http://nrs.harvard.edu/urn-3:HUL.InstRepos:14117760
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CONFOUNDING ADJUSTMENT IN COMPARATIVE STUDIES OF
NEWLY MARKETED MEDICATIONS

HIRAKU KUMAMARU

A dissertation submitted to the Faculty of
The Harvard School of Public Health
in partial fulfillment of the Requirements
for the degree of Doctor of Science
in the Department of Epidemiology
Harvard University
Boston, Massachusetts

March 2015
Dissertation Advisor: Dr. Sebastian Schneeweiss Hiraku Kumamaru

Confounding adjustment in comparative studies of newly marketed medications

Abstract

Observational studies of newly marketed medications can add important safety and
effectiveness information to what is known from pre-approval randomized controlled trials. As
new medications are often prescribed to select group of patients, confounding can be particularly
large in this setting. Multivariable modeling of the exposure or the outcome is commonly used to
reduce confounding, but the modeling can be challenged by rapidly evolving prescribing patterns
and the limited number of patients exposed to the new medication. Furthermore, while
adjustment for empirically identified potential confounders, and proxies thereof, has been shown
to reduce confounding, it is difficult to empirically identify potential confounders in settings with
small number of outcomes.
In this thesis, we explored two new methods to improve confounding control in the
setting of newly marketed medications with few exposures and outcomes and many potential
confounders. The first method is the high dimensional disease risk score (DRS) developed using
an historical cohort of comparator drug initiators. We developed prediction models for the
outcomes of interest in an historical cohort and applied the models to the concurrent study cohort
of new and comparator drug initiators. We then used individual patient’s predicted risk score to
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balance the baseline risk between the two groups. In chapter 1, we compared combinations of
shrinkage and dimension reduction approaches to reduce model over-fitting when developing
DRSs from large numbers of potential covariates. In chapter 2, we compared the performance of
the high dimensional DRSs to the standard high dimensional propensity score (hdPS) approach.
In chapter 3, we developed a new method that augments the hdPS variable selection process
with data from an historical cohort and compared this approach to standard hdPS. All
evaluations were conducted in example comparative studies of newly marketed medications
using large US claims database.
The hdPS with variable selection augmented by historical data showed good performance
in confounding adjustment even in small outcome settings. Future studies should evaluate the use
of this method in other settings and should explore improvements in the use of high dimensional
DRSs.

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Table of Contents
Title .............................................................................................................................................. i
Abstract ...................................................................................................................................... ii
Table of Contents ....................................................................................................................... iv
List of Figures ............................................................................................................................. v
List of Tables ............................................................................................................................. vi
Acknowledgements ................................................................................................................. viii
Chapter 1 Dimension reduction and shrinkage methods for high dimensional
disease risk score in historical data
Abstract ................................................................................................................................. 2
References ............................................................................................................................ 15
Tables ................................................................................................................................... 19
Figure ................................................................................................................................... 27
Chapter 2 Comparison of high dimensional confounder summary scores in database
studies of newly marketed medications
Abstract ............................................................................................................................... 33
References ............................................................................................................................ 47
Tables ................................................................................................................................... 49
Figures.................................................................................................................................. 56
Chapter 3 Historical data for augmenting high dimensional covariate selection in
database studies of newly marketed medications
Abstract ............................................................................................................................... 64
References ............................................................................................................................ 80
Tables ................................................................................................................................... 83
Figure ................................................................................................................................... 89

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LIST OF FIGURES

Figure I-1. Patient Enrollment and Follow-up in the Two Example Studies .......................... 27
Figure II-1. Schema of the Two Example Study Cohorts ....................................................... 56
Figure II-2. Distribution of estimated hdDRS and hdPS by drug group among the three
study example concurrent cohorts .......................................................................................... 57
Figure II-2. Calibration Plots of the expected risk predicted by hdDRS modeled in
historical data and observed risk of the outcomes in the three study examples ....................... 58
Figure III-1. Schema of Patient Enrollment to the Concurrent and Historical Cohorts .......... 89

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LIST OF TABLES

Table I-1. Baseline characteristics and observed risk of major hemorrhagic events within
180 days of warfarin and dabigatran initiators in the historical and concurrent cohorts .......... 19
Table I-2. Baseline characteristics and observed risk of GI bleeds within 180 days of
non-selective nonsteroidal anti-inflammatory drugs and cyclooxygenase-2 inhibitors
initiators in the historical and concurrent cohorts ..................................................................... 20
Table I-3. Predictive performance of the Disease risk score (DRS) models in warfarin vs.
dabigatran historical and concurrent cohorts ............................................................................ 21
Table I-4. Predictive performance of the Disease risk score (DRS) models in
cyclooxygenase-2 inhibitor vs. non-selective non-steroidal anti-inflammatory drugs
historical and concurrent cohorts .............................................................................................. 23
Table I-5. The relative odds of major hemorrhagic events within 180 days for dabigatran
initiators compared to warfarin initiators adjusted by DRS decile stratification ...................... 25
Table I-6. The relative odds of gastrointestinal bleeds within 180 days for coxibs
initiators compared to nsNSAIDs initiators adjusted by DRS decile stratification .................. 26
Table II-1. Baseline characteristics and observed risk of major hemorrhagic events and
deaths within 180days in warfarin and dabigatran initiators in the historical and
concurrent cohorts ..................................................................................................................... 49
Table II-2. Baseline characteristics and observed risk of GI bleeds within 180 days of
the non-selective nonsteroidal anti-inflammatory drugs and cyclooxygenase-2 inhibitors
initiators in the historical and concurrent cohorts ..................................................................... 52
Table II-3. Discrimination and calibration of the historically-developed hdDRS in the
concurrent cohorts
Table II-4. Estimated Relative Risks (Odds Ratios) adjusted by conventional regression
and by hdPS and hdDRS stratification without percentile trimming ........................................ 53
Table II-5. Number of patients and events by drug group and Estimated Odds Ratios
adjusted by hdPS and hdDRS stratification after 2.5-97.5 percentile trimming ....................... 54
Table II-6. Number of observations and events by drug group and Estimated Odds
Ratios and 95% confidence intervals adjusted by historical outcome predictor (op-) hdPS
stratification with and without 2.5-97.5 percentile trimming ................................................... 55
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Table III-1. Baseline characteristics and observed Risk of Death within 180 days in
initiators of warfarin and dabigatran in historical and concurrent cohorts ............................... 83
Table III-2. Baseline characteristics and observed Risk of GI bleeds within 180 days in
patients initiating non-selective non-steroidal anti-inflammatory drugs and
cyclooxygenase-2 inhibitors in the historical and concurrent cohorts ...................................... 84
Table III-3. Characteristics of the sampled cohorts of dabigatran and warfarin initiators,
and relative risk estimates (odds ratios) from stratified PS and hdPSs analyses ...................... 85
Table III-4. Characteristics of the sampled cohorts of cyclooxygenase-2 inhibitors and
non-selective non-steroidal anti-inflammatory drugs initiators, and relative risk estimates
(odds ratios) from stratified PS, hdPSs and hdDRS analyses ................................................... 86
Table III-5. Average Percent Overlap between the Variables Selected in the Full Cohort
vs. those selected in the Sampled cohorts for each Empirical Selection Procedure. ................ 87
Table III-6. Average Number of top 100 Variables Selected by the standard bias based
hdPS in the Full Cohort which were included in the 300 variables selected in Sampled
cohorts for each Empirical Selection Procedure ....................................................................... 88

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Acknowledgements
I am grateful to many people for supporting my completion of the doctoral
program at Harvard School of Public Health. I appreciate all of the invaluable
contributions from my family, friends, colleagues and mentors.
First and foremost, I would like to express my sincere gratitude to my adviser
Sebastian Schneeweiss, MD, ScD for his excellent mentorship and for welcoming me
into the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and
Women’s hospital and Harvard Medical School, and for providing me the opportunity
and support for my Pharmacoepidemiology training. I am also greatly thankful to all the
other members of my research committee; Joshua J. Gagne, PharmD, ScD for his close
and patient mentorship that really was essential for my completion of the dissertation;
Robert J. Glynn, ScD, PhD for his insightful and generous scientific inputs that greatly
improved the quality of my papers; Soko Setoguchi, MD DrPH, for inviting me into the
field of pharmacoepidemiology, and for thoughtfully guiding my academic development.
My appreciation also goes to division Chief Jerry Avorn, MD and all of the
members at the Division of Pharacoeidemiology and Pharmacoeconomics for their
support and the inspirational discussions during my study. I owe special thanks to Jessica
Franklin, PhD for her biostatistical consultations, and to Jun Liu, MD MPH and Helen
Mogun, MS for their help in programming. I would also like to thank my oral
examination committee members John D. Seeger, PharmD, DrPH, Robert J Glynn ScD,
PhD and Joel S. Weissman, PhD who provided helpful feedback to my dissertation
proposal.
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I am also very grateful to Sonia Hernandez-Diaz, MD, DrPH and to the
pharmacoepidemiology program at Harvard School of Public Health, and to Honjo
International Scholarship Foundation for providing the support during my study as a
doctoral student.
Finally, I would like to thank my wife Kanako, and my two children Amane and
Akari for their loving support and patience during the past years while I worked on my
study. Their presence filled my life in Boston with joy and good memories even during
the challenging times.

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