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Marginal models in analysis of correlated binary data with time dependent covariates

This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars. While the models presented in...

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Detalles Bibliográficos
Autores principales: Wilson, Jeffrey R, Vazquez-Arreola, Elsa, Chen, (Din) Ding-Geng
Lenguaje:eng
Publicado: Springer 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-030-48904-5
http://cds.cern.ch/record/2740596
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author Wilson, Jeffrey R
Vazquez-Arreola, Elsa
Chen, (Din) Ding-Geng
author_facet Wilson, Jeffrey R
Vazquez-Arreola, Elsa
Chen, (Din) Ding-Geng
author_sort Wilson, Jeffrey R
collection CERN
description This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars. While the models presented in the volume are applied to health and health-related data, they can be used to analyze any kind of data that contain covariates that change over time. The included data are analyzed with the use of both R and SAS, and the data and computing programs are provided to readers so that they can replicate and implement covered methods. It is an excellent resource for scholars of both computational and methodological statistics and biostatistics, particularly in the applied areas of health.
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spelling cern-27405962021-04-21T16:45:44Zdoi:10.1007/978-3-030-48904-5http://cds.cern.ch/record/2740596engWilson, Jeffrey RVazquez-Arreola, ElsaChen, (Din) Ding-GengMarginal models in analysis of correlated binary data with time dependent covariatesMathematical Physics and MathematicsThis monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars. While the models presented in the volume are applied to health and health-related data, they can be used to analyze any kind of data that contain covariates that change over time. The included data are analyzed with the use of both R and SAS, and the data and computing programs are provided to readers so that they can replicate and implement covered methods. It is an excellent resource for scholars of both computational and methodological statistics and biostatistics, particularly in the applied areas of health.Springeroai:cds.cern.ch:27405962020
spellingShingle Mathematical Physics and Mathematics
Wilson, Jeffrey R
Vazquez-Arreola, Elsa
Chen, (Din) Ding-Geng
Marginal models in analysis of correlated binary data with time dependent covariates
title Marginal models in analysis of correlated binary data with time dependent covariates
title_full Marginal models in analysis of correlated binary data with time dependent covariates
title_fullStr Marginal models in analysis of correlated binary data with time dependent covariates
title_full_unstemmed Marginal models in analysis of correlated binary data with time dependent covariates
title_short Marginal models in analysis of correlated binary data with time dependent covariates
title_sort marginal models in analysis of correlated binary data with time dependent covariates
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-030-48904-5
http://cds.cern.ch/record/2740596
work_keys_str_mv AT wilsonjeffreyr marginalmodelsinanalysisofcorrelatedbinarydatawithtimedependentcovariates
AT vazquezarreolaelsa marginalmodelsinanalysisofcorrelatedbinarydatawithtimedependentcovariates
AT chendindinggeng marginalmodelsinanalysisofcorrelatedbinarydatawithtimedependentcovariates