Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void,
Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data. After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint models, and incomplete data. Each of these sections begins with an introductory chapter that provides useful background material and a broad outline to set the stage for subsequent chapters. Rather than focus on a narrowly defined topic, chapters integrate important research discussions from the statistical literature. They seamlessly blend theory with applications and include examples and case studies from various disciplines.
Destined to become a landmark publication in the field, this carefully edited collection emphasizes statistical models and methods likely to endure in the future. Whether involved in the development of statistical methodology or the analysis of longitudinal data, readers will gain new perspectives on the field.
Author(s): Garrett Fitzmaurice, Marie Davidian, Geert Verbeke, Geert Molenberghs
Series: Chapman & Hall/CRC Handbooks of Modern Statistical Methods
Edition: 1
Publisher: Chapman and Hall/CRC
Year: 2008
Language: English
Pages: 633
Cover......Page 1
Title......Page 4
Copyright......Page 5
Dedication......Page 6
Contents......Page 7
Preface......Page 9
Editors......Page 11
Contributors......Page 13
PART I: Introduction and Historical Overview......Page 16
CHAPTER 1: Advances in longitudinal data analysis: An historical perspective......Page 18
PART II: Parametric Modeling of Longitudinal Data......Page 44
CHAPTER 2: Parametric modeling of longitudinal data: Introduction and overview......Page 46
CHAPTER 3: Generalized estimating equations for longitudinal data analysis......Page 58
CHAPTER 4: Generalized linear mixed-effects models......Page 94
CHAPTER 5: Non-linear mixed-effects models......Page 122
CHAPTER 6: Growth mixture modeling: Analysis with non-Gaussian random effects......Page 158
CHAPTER 7: Targets of inference in hierarchical models for longitudinal data......Page 182
PART III: Non-Parametric and Semi-Parametric Methods for Longitudinal Data......Page 204
CHAPTER 8: Non-parametric and semi-parametric regression methods: Introduction and overview......Page 206
CHAPTER 9: Non-parametric and semi-parametric regression methods for longitudinal data......Page 214
CHAPTER 10: Functional modeling of longitudinal data......Page 238
CHAPTER 11: Smoothing spline models for longitudinal data......Page 268
CHAPTER 12: Penalized spline models for longitudinal data......Page 306
PART IV: Joint Models for Longitudinal Data......Page 332
CHAPTER 13: Joint models for longitudinal data: Introduction and overview......Page 334
CHAPTER 14: Joint models for continuous and discrete longitudinal data......Page 342
CHAPTER 15: Random-effects models for joint analysis of repeated-measurement and time-to-event outcomes......Page 364
CHAPTER 16: Joint models for high-dimensional longitudinal data......Page 382
PART V: Incomplete Data......Page 408
CHAPTER 17: Incomplete data: Introduction and overview......Page 410
CHAPTER 18: Selection and pattern-mixture models......Page 424
CHAPTER 19: Shared-parameter models......Page 448
CHAPTER 20: Inverse probability weighted methods......Page 468
CHAPTER 21: Multiple imputation......Page 492
CHAPTER 22: Sensitivity analysis for incomplete data......Page 516
CHAPTER 23: Estimation of the causal effects of time-varying exposures......Page 568
Author Index......Page 616
Subject Index......Page 628
