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Models for discrete longitudinal data

Author: Geert Molenberghs; Geert Verbeke
Publisher: New York : Springer, ©2006.
Series: Springer series in statistics.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book.

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Geert Molenberghs; Geert Verbeke
ISBN: 0387251448 9780387251448
OCLC Number: 547795141
Description: xxii, 683 pages : illustrations ; 24 cm.
Contents: Introduction --
Motivating studies --
Generalized linear models --
Linear mixed models for Gaussian longitudinal data --
Model families --
The strength of marginal models --
Likelihood-based marginal models --
Generalized estimating equations --
Pseudo-likelihood --
Fitting marginal models with SAS --
Conditional models --
Pseudo-likehood --
From subject-specific to random-effects models --
The generalized linear mixed model (GLMM) --
Fitting generalized linear mixed models with SAS --
Marginal versus random-effects models. The analgesic trial --
Ordinal data --
The epilepsy data --
Non-linear models --
Pseudo-likelihood for a hierarchical model --
Random-effects models with serial correlation --
Non-Gaussian random effects --
Joint continuous and discrete responses --
High-dimensional joint models --
Missing data concepts --
Simple methods, direct likelihood, and WGEE --
Multiple imputation and the EM algorithm --
Selection models --
Pattern-mixture models --
Sensitivity analysis --
Incomplete data and SAS.
Series Title: Springer series in statistics.
Responsibility: Geert Molenberghs, Geert Verbeke.
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