Models for Discrete Longitudinal Data / Edition 1

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This book provides a comprehensive treatment on modeling approaches for non-Gaussian repeated measures, possibly subject to incompleteness. The authors begin with models for the full marginal distribution of the outcome vector. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. Several frequently used procedures for model fitting are discussed and differences between marginal models and random-effects models are given attention. The authors consider a variety of extensions, such as models for multivariate longitudinal measurements, random-effects models with serial correlation, and mixed models with non-Gaussian random effects. They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The text is organized so the reader can skip the software-oriented chapters and sections without breaking the logical flow.

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Editorial Reviews

From the Publisher
From the reviews:

"Strengths of this book include its breadth of topics, excellent organization and clarity of writing...I highly recommend this book to my colleagues and students." -Justine Shults for the Journal of Biopharmaceutical Statistics, Issue 3, 2006

"Models for Discrete Longitudinal Data is an excellent choice for any statistician with an interest in analyzing discrete longitudinal data. It covers all of the theoretical and applied aspects in this area and is organized in such a way to serve as a handy reference guide for applied statisticians, especially those in biomedical fields. I learned a great deal from this book, and I recommend it highly to others." -John Williamson for the Journal of the American Statistical Association, September 2006

"This book complements Verbeke and Molenberghs (2000), which focused on models based on the multivariate normal distribution. … This book covers the alternative models and approaches in a methodical and accessible manner. The emphasis in the book is on presenting methods for solving practical problems, and the authors succeed admirably in this. … The material is clearly presented … . This book is very welcome, and will undoubtedly prove to be useful and influential." (B. J. T. Morgan, Short Book Reviews, Vol. 26 (2), 2006)

"This book provides a comprehensive treatment of modeling approaches for non-Gaussian repeated measures … . the book shows how the different approaches can be implemented within the SAS software package. The text is so organized that the reader can skip the software-oriented chapters and sections without breaking the logical flow. … It is a very important, modern and useful book for statisticians." (T. Postelnicu, Zentralblatt MATH, Vol. 1093 (19), 2006)

"This book … concentrates on models for non-normally distributed longitudinal data, like binary or categorical data. … The book under review is a comprehensive collection of latest models for non-normally distributed longitudinal data. … Models for Discrete Longitudinal Data addresses interested (and experienced) students and lectures as well as practitioners looking for solutions of everyday problems." (K. Webel, Advances in Statistical Analysis, Vol. 91 (2), 2007)

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Product Details

  • ISBN-13: 9780387251448
  • Publisher: Springer New York
  • Publication date: 8/4/2005
  • Series: Springer Series in Statistics
  • Edition description: 1st ed. 2005. Corr. 2nd printing 2006
  • Edition number: 1
  • Pages: 687
  • Product dimensions: 9.21 (w) x 6.14 (h) x 1.56 (d)

Table of Contents

1 Introduction 3
2 Motivating studies 7
3 Generalized linear models 27
4 Linear mixed models for Gaussian longitudinal data 35
5 Model families 45
6 The strength of marginal models 55
7 Likelihood-based marginal models 83
8 Generalized estimating equations 151
9 Pseudo-likelihood 189
10 Fitting marginal models with SAS 203
11 Conditional models 225
12 Pseudo-likehood 243
13 From subject-specific to random-effects models 257
14 The generalized linear mixed model (GLMM) 265
15 Fitting generalized linear mixed models with SAS 281
16 Marginal versus random-effects models 297
17 The analgesic trial 309
18 Ordinal data 325
19 The epilepsy data 337
20 Non-linear models 347
21 Pseudo-likelihood for a hierarchical model 393
22 Random-effects models with serial correlation 405
23 Non-Gaussian random effects 419
24 Joint continuous and discrete responses 437
25 High-dimensional joint models 467
26 Missing data concepts 481
27 Simple methods, direct likelihood, and WGEE 489
28 Multiple imputation and the EM algorithm 511
29 Selection models 531
30 Pattern-mixture models 555
31 Sensitivity analysis 575
32 Incomplete data and SAS 607
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