Fitzmaurice G., Davidian M., Verbeke G., Molenberghs G. (eds.) Longitudinal Data Analysis

New York: Chapman and Hall/CRC, 2008. — 633 p.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.Copyright
DedicationEditorsIntroduction and Historical Overview
Advances in longitudinal data analysis An historical perspective
Parametric Modeling of Longitudinal Data
Parametric modeling of longitudinal data Introduction and overview
Generalized estimating equations for longitudinal data analysis
Generalized linear mixed-effects models
Non-linear mixed-effects models
Growth mixture modeling Analysis with non-Gaussian random effects
Targets of inference in hierarchical models for longitudinal data
Non-Parametric and Semi-Parametric Methods for Longitudinal Data
Non-parametric and semi-parametric regression methods Introduction and overview
Non-parametric and semi-parametric regression methods for longitudinal data
Functional modeling of longitudinal data
Smoothing spline models for longitudinal data
Penalized spline models for longitudinal data
Joint Models for Longitudinal Data
Joint models for longitudinal data Introduction and overview
Joint models for continuous and discrete longitudinal data
Random-effects models for joint analysis of repeated-measurement and time-to-event outcomes
Joint models for high-dimensional longitudinal data
Incomplete Data
Incomplete data Introduction and overview
Selection and pattern-mixture models
Shared-parameter models
Inverse probability weighted methods
Multiple imputation
Sensitivity analysis for incomplete data
Estimation of the causal effects of time-varying exposures
Author Index
Subject Index