Fitzmaurice / Davidian / Verbeke Longitudinal Data Analysis

Erscheinungsjahr 2008
ISBN: 978-1-4200-1157-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 632 Seiten

Reihe: Chapman & Hall/CRC Handbooks of Modern Statistical Methods

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.

Zielgruppe

Researchers and graduate students in statistics and biostatistics; applied statisticians; quantitative researchers.

Autoren/Hrsg.

Fitzmaurice, Garrett

Davidian, Marie

Verbeke, Geert

Molenberghs, Geert

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

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.