Longitudinal Data Analysis

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.

Introduction and Historical Overview Advances in Longitudinal Data Analysis: A Historical Perspective Garrett Fitzmaurice and Geert Molenberghs Parametric Modeling of Longitudinal Data Parametric Modeling of Longitudinal Data: Introduction and Overview Garrett Fitzmaurice and Geert Verbeke Generalized Estimating Equations for Longitudinal Data Analysis Stuart Lipsitz and Garrett Fitzmaurice Generalized Linear Mixed-Effects Models Sophia Rabe-Hesketh and Anders Skrondal Nonlinear Mixed-Effects Models Marie Davidian Growth Mixture Modeling: Analysis with Non-Gaussian Random Effects Bengt Muthén and Tihomir Asparouhov Targets of Inference in Hierarchical Models for Longitudinal Data Stephen W. Raudenbush Nonparametric and Semiparametric Methods for Longitudinal Data Nonparametric and Semiparametric Regression Methods: Introduction and Overview Xihong Lin and Raymond J. Carroll Nonparametric and Semiparametric Regression Methods for Longitudinal Data Xihong Lin and Raymond J. Carroll Functional Modeling of Longitudinal Data Hans-Georg Müller Smoothing Spline Models for Longitudinal Data S.J. Welham Penalized Spline Models for Longitudinal Data Babette A. Brumback, Lyndia C. Brumback, and Mary J. Lindstrom Joint Models for Longitudinal Data Joint Models for Longitudinal Data: Introduction and Overview Geert Verbeke and Marie Davidian Joint Models for Continuous and Discrete Longitudinal Data Christel Faes, Helena Geys, and Paul Catalano Random-Effects Models for Joint Analysis of Repeated-Measurement and Time-to-Event Outcomes Peter Diggle, Robin Henderson, and Peter Philipson Joint Models for High-Dimensional Longitudinal Data Steffen Fieuws and Geert Verbeke Incomplete Data Incomplete Data: Introduction and Overview Geert Molenberghs and Garrett Fitzmaurice Selection and Pattern-Mixture Models Roderick Little Shared-Parameter Models Paul S. Albert and Dean A. Follmann Inverse Probability Weighted Methods Andrea Rotnitzky Multiple Imputation Michael G. Kenward and James R. Carpenter Sensitivity Analysis for Incomplete Data Geert Molenberghs, Geert Verbeke, and Michael G. Kenward Estimation of the Causal Effects of Time-Varying Exposures James M. Robins and Miguel A. Hernán Index About the Editors Garrett Fitzmaurice is Associate Professor of Psychiatry at the Harvard Medical School, Associate Professor of Biostatistics at the Harvard School of Public Health, and Foreign Adjunct Professor of Biostatistics at the Karolinska Institute in Sweden. He is a fellow of the American Statistical Association, a member of the International Statistical Institute, and a recipient of the American Statistical Association’s Excellence in Continuing Education Award. Marie Davidian is William Neal Reynolds Distinguished Professor of Statistics at North Carolina State University and Adjunct Professor of Biostatistics and Bioinformatics at Duke University. She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. Dr. Davidian is also a member of the International Statistical Institute and executive editor of Biometrics. Geert Verbeke is Professor of Biostatistics in the Biostatistical Centre at the Catholic University of Leuven in Belgium. He is a past president of the Belgian Region of the International Biometric Society, joint editor of the Journal of the Royal Statistical Society, Series A, and an international representative on the board of directors and a fellow of the American Statistical Association. Jointly with Geert Molenberghs, Dr. Verbeke twice received the American Statistical Association’s Excellence in Continuing Education Award. Geert Molenberghs is Professor of Biostatistics in the Center for Statistics at Hasselt University and in the Biostatistical Centre at the Catholic University of Leuven in Belgium. He is a fellow of the American Statistical Association, a member of the International Statistical Institute, a recipient of the Guy Medal in Bronze from the Royal Statistical Society, and coeditor of Biometrics. Together with Geert Verbeke, Dr. Molenberghs twice received the American Statistical Association’s Excellence in Continuing Education Award.