Models for Discrete Longitudinal Data / Edition 1

Models for Discrete Longitudinal Data / Edition 1

by Geert Molenberghs, Geert Verbeke
     
 

ISBN-10: 0387251448

ISBN-13: 9780387251448

Pub. Date: 08/04/2005

Publisher: Springer New York

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

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Overview

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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Product Details

ISBN-13:
9780387251448
Publisher:
Springer New York
Publication date:
08/04/2005
Series:
Springer Series in Statistics
Edition description:
1st ed. 2005. Corr. 2nd printing 2006
Pages:
687
Product dimensions:
9.21(w) x 6.14(h) x 1.56(d)

Related Subjects

Table of Contents

1Introduction3
2Motivating studies7
3Generalized linear models27
4Linear mixed models for Gaussian longitudinal data35
5Model families45
6The strength of marginal models55
7Likelihood-based marginal models83
8Generalized estimating equations151
9Pseudo-likelihood189
10Fitting marginal models with SAS203
11Conditional models225
12Pseudo-likehood243
13From subject-specific to random-effects models257
14The generalized linear mixed model (GLMM)265
15Fitting generalized linear mixed models with SAS281
16Marginal versus random-effects models297
17The analgesic trial309
18Ordinal data325
19The epilepsy data337
20Non-linear models347
21Pseudo-likelihood for a hierarchical model393
22Random-effects models with serial correlation405
23Non-Gaussian random effects419
24Joint continuous and discrete responses437
25High-dimensional joint models467
26Missing data concepts481
27Simple methods, direct likelihood, and WGEE489
28Multiple imputation and the EM algorithm511
29Selection models531
30Pattern-mixture models555
31Sensitivity analysis575
32Incomplete data and SAS607

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