The Resource Models for Discrete Longitudinal Data, by Geert Molenberghs, Geert Verbeke, (electronic resource)

Models for Discrete Longitudinal Data, by Geert Molenberghs, Geert Verbeke, (electronic resource)

Label
Models for Discrete Longitudinal Data
Title
Models for Discrete Longitudinal Data
Statement of responsibility
by Geert Molenberghs, Geert Verbeke
Creator
Contributor
Author
Author
Subject
Language
  • eng
  • eng
Summary
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. Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt in Belgium and has published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001–2004) and as Associate Editor for several journals, including Biometrics and Biostatistics. He was President of the International Biometric Society (2004–2005). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. Geert Verbeke is Professor of Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He has published a number of methodological articles on various aspects of models for longitudinal data analyses, with particular emphasis on mixed models. Geert Verbeke is Past President of the Belgian Region of the International Biometric Society, International Program Chair for the International Biometric Conference in Montreal (2006), and Joint Editor of the Journal of the Royal Statistical Society, Series A (2005–2008). He has served as Associate Editor for several journals including Biometrics and Applied Statistics. The authors also wrote a monograph on linear mixed models for longitudinal data (Springer, 2000) and 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.
Member of
Is Subseries of
http://library.link/vocab/creatorName
Molenberghs, Geert
Dewey number
519.53
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
  • HmMD_gp95II
  • x3NNix941_Y
Language note
English
LC call number
  • QA273.A1-274.9
  • QA274-274.9
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
Verbeke, Geert.
Series statement
Springer Series in Statistics,
http://library.link/vocab/subjectName
  • Distribution (Probability theory
  • Mathematical statistics
  • Statistics
  • Probability Theory and Stochastic Processes
  • Statistical Theory and Methods
  • Statistics for Life Sciences, Medicine, Health Sciences
Label
Models for Discrete Longitudinal Data, by Geert Molenberghs, Geert Verbeke, (electronic resource)
Instantiates
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
Contents
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
Dimensions
unknown
Edition
1st ed. 2005.
Extent
1 online resource (699 p.)
Form of item
online
Isbn
9780387289809
Media category
computer
Media type code
c
Other control number
10.1007/0-387-28980-1
Specific material designation
remote
System control number
  • (CKB)1000000000227785
  • (EBL)302779
  • (OCoLC)262680438
  • (SSID)ssj0000772063
  • (PQKBManifestationID)12342536
  • (PQKBTitleCode)TC0000772063
  • (PQKBWorkID)10816474
  • (PQKB)10188901
  • (SSID)ssj0000204116
  • (PQKBManifestationID)11175453
  • (PQKBTitleCode)TC0000204116
  • (PQKBWorkID)10174653
  • (PQKB)11586997
  • (DE-He213)978-0-387-28980-9
  • (MiAaPQ)EBC302779
  • (EXLCZ)991000000000227785
Label
Models for Discrete Longitudinal Data, by Geert Molenberghs, Geert Verbeke, (electronic resource)
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
Contents
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
Dimensions
unknown
Edition
1st ed. 2005.
Extent
1 online resource (699 p.)
Form of item
online
Isbn
9780387289809
Media category
computer
Media type code
c
Other control number
10.1007/0-387-28980-1
Specific material designation
remote
System control number
  • (CKB)1000000000227785
  • (EBL)302779
  • (OCoLC)262680438
  • (SSID)ssj0000772063
  • (PQKBManifestationID)12342536
  • (PQKBTitleCode)TC0000772063
  • (PQKBWorkID)10816474
  • (PQKB)10188901
  • (SSID)ssj0000204116
  • (PQKBManifestationID)11175453
  • (PQKBTitleCode)TC0000204116
  • (PQKBWorkID)10174653
  • (PQKB)11586997
  • (DE-He213)978-0-387-28980-9
  • (MiAaPQ)EBC302779
  • (EXLCZ)991000000000227785

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