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

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. 

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.

Dr. Jeffrey Wilson is associate professor of statistics and biostatistics at Arizona State University, where he has served as director of the School of Health Management and Policy at the W. P. Carey School of Business, and as director and co-director of the biostatistics core at the NIH Center at Banner/Arizona Alzheimers Consortium. Dr. Wilson is the statistics associate editor for The Journal of Minimally Invasive Gynecology, as well as former chair of the editorial board for the American Journal of Public Health. His research experience includes PI/co-PI roles with the National Science Foundation, the United States Department of l-