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Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates

Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates
Catalogue Information
Field name Details
Dewey Class 519.5
Title Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates ([EBook]) / by Jeffrey R. Wilson, Elsa Vazquez-Arreola, (Din) Ding-Geng Chen.
Author Wilson, Jeffrey R.
Added Personal Name Vazquez-Arreola, Elsa
Chen, (Din) Ding-Geng
Other name(s) SpringerLink (Online service)
Edition statement 1st ed. 2020.
Publication Cham : : Springer International Publishing : : Imprint: Springer, , 2020.
Physical Details XXIII, 166 p. 20 illus. : online resource.
Series Emerging Topics in Statistics and Biostatistics 2524-7735
ISBN 9783030489045
Summary Note 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.:
Contents note 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.
Mode of acces to digital resource Digital book. Cham Springer Nature 2020. - Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format
System details note - Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site https://doi.org/10.1007/978-3-030-48904-5
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