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＜電子ブック＞
Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates / by Jeffrey R. Wilson, Elsa Vazquez-Arreola, (Din) Ding-Geng Chen
(Emerging Topics in Statistics and Biostatistics. ISSN:25247743)

版	1st ed. 2020.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2020
本文言語	英語
大きさ	XXIII, 166 p. 20 illus : online resource
著者標目	*Wilson, Jeffrey R author Vazquez-Arreola, Elsa author Chen, (Din) Ding-Geng author SpringerLink (Online service)
件　名	LCSH:Biometry LCSH:Statistics  LCSH:Health services administration LCSH:Public health FREE:Biostatistics FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences FREE:Statistical Theory and Methods FREE:Health Care Management FREE:Public Health
一般注記	1. Introduction to Binary Regression Models -- 2. Generalized Estimating Equations Binary Models -- 3. Lai and Small Models for Time-Dependent Covariates -- 4. Lalonde, wilson, and Yin Models for Time-Dependent Covariates -- 5. Irimata, Broatch, and Wilson Models for Time-Dependent Covariates -- 6. Bayesian GMM to IBW Method of Analysis -- 7. Models for Joint Responses for Time-Dependent Covariates -- 8. Other Models for 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 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 HTTP:URL=https://doi.org/10.1007/978-3-030-48904-5

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