Models for discrete longitudinal data / Geert Molenberghs, Geert Verbeke.

By: Molenberghs, Geert Contributor(s): Verbeke, Geert Material type: Text

TextLanguage: English Series: Springer series in statisticsPublication details: New York ; London : Springer, c2006. Description: xxii, 683 p. : ill. 25 cmISBN: 0387251448 (hbk.); 9780387251448Subject(s): Multivariate analysis | Longitudinal methodDDC classification: 519.53 LOC classification: QA278 | .M65 2005Online resources: Publisher description | WorldCat details | Ebook Fulltext

Contents:

TOC 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.

Summary: Summary: The linear mixed model is the main parametric tool for the analysis of continuous longitudinal data. This book shows how the different approaches can be implemented within the SAS software package.

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Barcode
E-Book	EWU Library E-book	Non-fiction	519.53 MOM 2006 (Browse shelf(Opens below))		Not for loan
Text	EWU Library Reserve Section	Non-fiction	519.53 MOM 2006 (Browse shelf(Opens below))	C-1	Not For Loan	26628
Text	EWU Library Circulation Section	Non-fiction	519.53 MOM 2006 (Browse shelf(Opens below))	C-2	Available	26884
Text	EWU Library Circulation Section	Non-fiction	519.53 MOM 2006 (Browse shelf(Opens below))	C-3	Available	26885

Total holds: 0

Includes bibliographical references and index.

TOC 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.

Summary:
The linear mixed model is the main parametric tool for the analysis of continuous longitudinal data. This book shows how the different approaches can be implemented within the SAS software package.

Saifun Momota

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