Readership: Statisticans - MSc students and researchers
Peter Diggle, Department of Mathematics and Statistics, University of Lancaster, Patrick Heagerty, Biostatistics department University of Washington, Kung-Yee Liang, Biostatistics department, Johns Hopkins University, and Scott Zeger, Biostatistics department, Johns Hopkins University
". . . provides an excellent bridge between novel concepts in theoretical statistics and their potential use in applied research." - Statistics in Medicine, 23
"The topics covered are too numerous to dwell on here ... If your work involves longitudinal data and you wish to update, this book will serve you very well. As a quick look-up, it is very useful." - Pharmaceutical Statistics
"The authors conclude each chapter with a helpful summary or conclusion, often indicating further reading. Helpfully, they also mention the topics that they have chosen not to present, together with other recommended books for you to follow up ... They have also chosen a good selection of examples, many of them medical, with which the various methods are clearly illustrated." - Pharmaceutical Statistics
"Readers with interests across a wide spectrum of application areas will find the ideas relevant and interesting ... The book is readable and well written ... It belongs to the possession of every statistician who encounters longitudinal data." - Zentralblatt MATH
1: Introduction 2: Design considerations 3: Exploring longitudinal data 4: General linear models 5: Parametric models for covariance structure 6: Analysis of variance methods 7: Generalized linear models for longitudinal data 8: Marginal models 9: Random effects models 10: Transition models 11: Likelihood-based methods for categorical data 12: Time-dependent covariates 13: Missing values in longitudinal data 14: Additional topics Appendix Bibliography Index
