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Linear mixed models for longitudinal data

This paperback edition is a reprint of the 2000 edition. This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal m...

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Detalles Bibliográficos
Autores principales: Molenberghs, Geert, Verbeke, Geert
Lenguaje:eng
Publicado: Springer 2000
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-1-4419-0300-6
http://cds.cern.ch/record/2023533
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author Molenberghs, Geert
Verbeke, Geert
author_facet Molenberghs, Geert
Verbeke, Geert
author_sort Molenberghs, Geert
collection CERN
description This paperback edition is a reprint of the 2000 edition. This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogeity model, conditional linear mixed models). This book will be of interest to applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia. The book is explanatory rather than mathematically rigorous. Most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated. However, some other commercially available packages are discussed as well. Great care has been taken in presenting the data analyses in a software-independent fashion. Geert Verbeke is Professor in Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He is Past President of the Belgian Region of the International Biometric Society, a Board Member of the American Statistical Association, and past Joint Editor of the Journal of the Royal Statistical Society, Series A (2005--2008). He is the director of the Leuven Center for Biostatistics and statistical Bioinformatics (L-BioStat), and vice-director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), a joint initiative of the Hasselt and Leuven universities in Belgium. Geert Molenberghs is Professor of Biostatistics at Universiteit Hasselt and Katholieke Universiteit Leuven in Belgium. He was Joint Editor of Applied Statistics (2001-2004) and Co-Editor of Biometrics (2007-2009). He was President of the International Biometric Society (2004-2005), and has received the Guy Medal in Bronze from the Royal Statistical Society and the Myrto Lefkopoulou award from the Harvard School of Public Health. He is founding director of the Center for Statistics and also the director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics. Both authors have received the American Statistical Association's Excellence in Continuing Education Award in 2002, 2004, 2005, and 2008. Both are elected Fellows of the American Statistical Association and elected members of the International Statistical Institute.
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spelling cern-20235332021-04-21T20:12:51Zdoi:10.1007/978-1-4419-0300-6http://cds.cern.ch/record/2023533engMolenberghs, GeertVerbeke, GeertLinear mixed models for longitudinal dataMathematical Physics and MathematicsThis paperback edition is a reprint of the 2000 edition. This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogeity model, conditional linear mixed models). This book will be of interest to applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia. The book is explanatory rather than mathematically rigorous. Most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated. However, some other commercially available packages are discussed as well. Great care has been taken in presenting the data analyses in a software-independent fashion. Geert Verbeke is Professor in Biostatistics at the Biostatistical Centre of the Katholieke Universiteit Leuven in Belgium. He is Past President of the Belgian Region of the International Biometric Society, a Board Member of the American Statistical Association, and past Joint Editor of the Journal of the Royal Statistical Society, Series A (2005--2008). He is the director of the Leuven Center for Biostatistics and statistical Bioinformatics (L-BioStat), and vice-director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), a joint initiative of the Hasselt and Leuven universities in Belgium. Geert Molenberghs is Professor of Biostatistics at Universiteit Hasselt and Katholieke Universiteit Leuven in Belgium. He was Joint Editor of Applied Statistics (2001-2004) and Co-Editor of Biometrics (2007-2009). He was President of the International Biometric Society (2004-2005), and has received the Guy Medal in Bronze from the Royal Statistical Society and the Myrto Lefkopoulou award from the Harvard School of Public Health. He is founding director of the Center for Statistics and also the director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics. Both authors have received the American Statistical Association's Excellence in Continuing Education Award in 2002, 2004, 2005, and 2008. Both are elected Fellows of the American Statistical Association and elected members of the International Statistical Institute.Springeroai:cds.cern.ch:20235332000
spellingShingle Mathematical Physics and Mathematics
Molenberghs, Geert
Verbeke, Geert
Linear mixed models for longitudinal data
title Linear mixed models for longitudinal data
title_full Linear mixed models for longitudinal data
title_fullStr Linear mixed models for longitudinal data
title_full_unstemmed Linear mixed models for longitudinal data
title_short Linear mixed models for longitudinal data
title_sort linear mixed models for longitudinal data
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-1-4419-0300-6
http://cds.cern.ch/record/2023533
work_keys_str_mv AT molenberghsgeert linearmixedmodelsforlongitudinaldata
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