Molenberghs / Verbeke Models for Discrete Longitudinal Data
1. Auflage 2006
ISBN: 978-0-387-28980-9
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 687 Seiten, eBook
Reihe: Springer Series in Statistics
ISBN: 978-0-387-28980-9
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
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. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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 Weighted Generalized Estimating Equations.- Multiple Imputation and the Expectation-Maximization Algorithm.- Selection Models.- Pattern-mixture Models.- Sensitivity Analysis.- Incomplete Data and SAS.
