Joint models for longitudinal and time-to-event data : with applications in R

274 Pages·2012·3.07 MB·English

by  Dimitris Rizopoulos

Statistics
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In longitudinal studies it is often of interest to investigate how a marker that is
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repeatedly measured in time is associated with a time to an event of interest, e.g., mi
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prostate cancer studies where longitudinal PSA level measurements are collected e
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in conjunction with the time-to-recurrence. Joint Models for Longitudinal and -
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Time-to-Event Data: With Applications in R provides a full treatment of random
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effects joint models for longitudinal and time-to-event outcomes that can be -o
utilized to analyze such data. The content is primarily explanatory, focusing on Ed Joint Models for
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applications of joint modeling, but sufficient mathematical details are provided to v
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facilitate understanding of the key features of these models. s
n Longitudinal and
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Features to

• Provides a complete treatment of joint models for longitudinal and time-to- Dr
Time-to-Event Data
event data aL
• Introduces various extensions of the standard joint model, including to
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several parameterizations for the association structure and the handling of n

competing risks g With Applications in R
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• Covers several diagnostic tools based on residuals to assess the t
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assumptions behind a joint model
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• Discusses dynamic predictions for the survival and longitudinal outcomes,
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and discrimination concepts for longitudinal markers n
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• Emphasizes applications so readers understand the type of research
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questions best answered with joint models
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All illustrations put forward can be implemented in the R programming language
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via the freely available package JM written by the author.

About the Author
Dimitris Rizopoulos is an Assistant Professor at the Department of Biostatistics
of the Erasmus University Medical Center in the Netherlands. Dr. Rizopoulos
received his M.Sc. in Statistics in 2003 from the Athens University of Economics
and Business, and a Ph.D. in Biostatistics in 2008 from the Katholieke Universiteit
Leuven. R
i
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Dr. Rizopoulos wrote his dissertation, as well as a number of methodological o
articles on various aspects of joint models for longitudinal and time-to-event p
o
Dimitris Rizopoulos
data. He currently serves as an Associate Editor for Biometrics and Biostatistics, u
l
and has been a guest editor for a special issue in joint modeling techniques in o
s
Statistical Methods in Medical Research.
K13371
K13371_Cover.indd 1 5/15/12 10:38 AM
Joint Models for
Longitudinal and
Time-to-Event Data
With Applications in R
K13371_FM.indd 1 5/9/12 1:36 PM
Editor-in-Chief
Shein-Chung Chow, Ph.D.
Professor
Department of Biostatistics and Bioinformatics
Duke University School of Medicine
Durham, North Carolina
Series Editors
Byron Jones Jen-pei Liu
Biometrical Fellow Professor
Statistical Methodology Division of Biometry
Department of Agronomy
Integrated Information Sciences
National Taiwan University
Novartis Pharma AG
Taipei, Taiwan
Basel, Switzerland

Karl E. Peace Bruce W. Turnbull
Georgia Cancer Coalition Professor
Distinguished Cancer Scholar School of Operations Research
Senior Research Scientist and and Industrial Engineering
Professor of Biostatistics Cornell University
Jiann-Ping Hsu College of Public Health Ithaca, New York
Georgia Southern University
Statesboro, Georgia

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Adaptive Design Methods in Clinical Trial Methodology
Clinical Trials, Second Edition Karl E. Peace and Ding-Geng (Din) Chen
Shein-Chung Chow and Mark Chang
Computational Methods in Biomedical
Adaptive Design Theory and Research
Implementation Using SAS and R Ravindra Khattree and Dayanand N. Naik
Mark Chang
Computational Pharmacokinetics
Advanced Bayesian Methods for Medical Anders Källén
Test Accuracy
Confidence Intervals for Proportions and
Lyle D. Broemeling
Related Measures of Effect Size
Advances in Clinical Trial Biostatistics Robert G. Newcombe
Nancy L. Geller
Controversial Statistical Issues in
Basic Statistics and Pharmaceutical Clinical Trials
Statistical Applications, Second Edition Shein-Chung Chow
James E. De Muth
Data and Safety Monitoring Committees
Bayesian Adaptive Methods for in Clinical Trials
Clinical Trials Jay Herson
Scott M. Berry, Bradley P. Carlin,
Design and Analysis of Animal Studies in
J. Jack Lee, and Peter Muller
Pharmaceutical Development
Bayesian Analysis Made Simple: An Excel Shein-Chung Chow and Jen-pei Liu
GUI for WinBUGS
Design and Analysis of Bioavailability and
Phil Woodward
Bioequivalence Studies, Third Edition
Bayesian Methods for Measures of Shein-Chung Chow and Jen-pei Liu
Agreement
Design and Analysis of Bridging Studies
Lyle D. Broemeling
Jen-pei Liu, Shein-Chung Chow,
Bayesian Missing Data Problems: EM, and Chin-Fu Hsiao
Data Augmentation and Noniterative
Design and Analysis of Clinical Trials with
Computation
Time-to-Event Endpoints
Ming T. Tan, Guo-Liang Tian,
Karl E. Peace
and Kai Wang Ng
Design and Analysis of Non-Inferiority
Bayesian Modeling in Bioinformatics
Trials
Dipak K. Dey, Samiran Ghosh,
Mark D. Rothmann, Brian L. Wiens,
and Bani K. Mallick
and Ivan S. F. Chan
Biostatistics: A Computing Approach
Difference Equations with Public Health
Stewart J. Anderson
Applications
Causal Analysis in Biomedicine and Lemuel A. Moyé and Asha Seth Kapadia
Epidemiology: Based on Minimal
DNA Methylation Microarrays:
Sufficient Causation
Experimental Design and Statistical
Mikel Aickin
Analysis
Clinical Trial Data Analysis using R Sun-Chong Wang and Arturas Petronis
Ding-Geng (Din) Chen and Karl E. Peace
K13371_FM.indd 3 5/9/12 1:36 PM
DNA Microarrays and Related Genomics Monte Carlo Simulation for the
Techniques: Design, Analysis, and Pharmaceutical Industry: Concepts,
Interpretation of Experiments Algorithms, and Case Studies
David B. Allison, Grier P. Page, Mark Chang
T. Mark Beasley, and Jode W. Edwards
Multiple Testing Problems in
Dose Finding by the Continual Pharmaceutical Statistics
Reassessment Method Alex Dmitrienko, Ajit C. Tamhane,
Ying Kuen Cheung and Frank Bretz
Elementary Bayesian Biostatistics Randomized Clinical Trials of
Lemuel A. Moyé Nonpharmacological Treatments
Isabelle Boutron, Philippe Ravaud, and
Frailty Models in Survival Analysis
David Moher
Andreas Wienke
Sample Size Calculations in Clinical
Generalized Linear Models: A Bayesian
Research, Second Edition
Perspective
Shein-Chung Chow, Jun Shao
Dipak K. Dey, Sujit K. Ghosh,
and Hansheng Wang
and Bani K. Mallick
Statistical Design and Analysis of
Handbook of Regression and Modeling:
Stability Studies
Applications for the Clinical and
Shein-Chung Chow
Pharmaceutical Industries
Daryl S. Paulson Statistical Evaluation of Diagnostic
Performance: Topics in ROC Analysis
Interval-Censored Time-to-Event Data:
Kelly H. Zou, Aiyi Liu, Andriy Bandos,
Methods and Applications
Lucila Ohno-Machado, and Howard Rockette
Ding-Geng (Din) Chen, Jianguo Sun,
and Karl E. Peace Statistical Methods for Clinical Trials
Mark X. Norleans
Joint Models for Longitudinal and Time-
to-Event Data: With Applications in R Statistics in Drug Research:
Dimitris Rizopoulos Methodologies and Recent
Developments
Measures of Interobserver Agreement
Shein-Chung Chow and Jun Shao
and Reliability, Second Edition
Mohamed M. Shoukri Statistics in the Pharmaceutical Industry,
Third Edition
Medical Biostatistics, Third Edition
Ralph Buncher and Jia-Yeong Tsay
A. Indrayan
Translational Medicine: Strategies and
Meta-Analysis in Medicine and Health
Statistical Methods
Policy
Dennis Cosmatos and Shein-Chung Chow
Dalene Stangl and Donald A. Berry
K13371_FM.indd 4 5/9/12 1:36 PM
Joint Models for
Longitudinal and
Time-to-Event Data
With Applications in R
Dimitris Rizopoulos
K13371_FM.indd 5 5/9/12 1:36 PM
CRC Press
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To Roula and my parents
To the memory of my grandmother
TThhiiss ppaaggee iinntteennttiioonnaallllyy lleefftt bbllaannkk
Preface
Joint models for longitudinal and time-to-event data have become a valuable
tool in the analysis of follow-up data. These models are applicable mainly in
two settings: First, when the focus is on the survival outcome and we wish to
account for the effect of an endogenous time-dependent covariate measured
with error, and second, when the focus is on the longitudinal outcome and
we wish to correct for nonrandom dropout. Due to the capability of joint
models to provide valid inferences in settings where simpler statistical tools
fail to do so, and their wide range of applications, the last 25 years have
seen many advances in the joint modeling field. Even though interest and
developments in joint models have been widespread, information about them
has been equally scattered in articles, presenting recent advances in the field,
andinbookchaptersinafewtextsdedicatedeithertolongitudinalorsurvival
dataanalysis.However,nosinglemonographortextdedicatedtothistypeof
models seems to be available.
The purpose in writing this book, therefore, is to provide an overview of
the theory and application of joint models for longitudinal and survival data.
In the literature two main frameworks have been proposed, namely the ran-
dom effects joint model that uses latent variables to capture the associations
between the two outcomes (Tsiatis and Davidian, 2004), and the marginal
structural joint models based on G estimators (Robins et al., 1999, 2000).
In this book we focus on the former. Both subfields of joint modeling, i.e.,
handling of endogenous time-varying covariates and nonrandom dropout, are
equally covered and presented in real datasets. Motivated by the background
and collaborations of the author, these examples come from the medical field
and in particular from biomarker research. Nevertheless, joint models of this
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