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Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates / by Jeffrey R. Wilson, Elsa Vazquez-Arreola, (Din) Ding-Geng Chen

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Emerging Topics in Statistics and BiostatisticsPublisher: Cham : Springer International Publishing, 2020Publisher: Cham : Imprint: Springer, 2020Edition: 1st ed. 2020Description: 1 Online-Ressource(XXIII, 166 p. 20 illus.)ISBN:
  • 9783030489045
Subject(s): Additional physical formats: 9783030489038 | 9783030489052 | 9783030489069 | Erscheint auch als: 9783030489038 Druck-Ausgabe | Erscheint auch als: 9783030489052 Druck-Ausgabe | Erscheint auch als: 9783030489069 Druck-AusgabeDOI: DOI: 10.1007/978-3-030-48904-5Online resources: Summary: 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.Summary: 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.PPN: PPN: 1734624132Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS
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