Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates (eBook) von Jeffrey R. Wilson

Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates
Der Artikel wird am Ende des Bestellprozesses zum Download zur Verfügung gestellt.

53,49 €* eBook

ISBN-13:
9783030489045
Veröffentl:
2020
Einband:
eBook
Seiten:
166
Autor:
Jeffrey R. Wilson
Serie:
Emerging Topics in Statistics and Biostatistics
eBook Format:
PDF
eBook-Typ:
Reflowable eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Inhaltsverzeichnis
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.
Beschreibung

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.


Autor

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 forThe Journal of Minimally Invasive Gynecology, as well as former chair of the editorial board for theAmerican Journal of Public Health. His research experience includes PI/co-PI roles with the National Science Foundation, the United States Department of Agriculture, the National Institute of Health, the Arizona Department of Health Services, and the Arizona Disease Research Commission. He has published more than 65 articles in prominent academic journals, and has consulted with pharmaceutical companies and hospitals while representing them before the FDA and other federal government healthcare agencies.

Ms. Elsa Vazquez is a PhD candidate in statistics at Arizona State University. She holds a BS in applied mathematics from Juarez University of the State of Durango, and a MS in mathematics from Arizona State University. Ms. Vazquez works as a statistician at the Center for Applied Behavioral Health Policy, where she collaborates with its research and evaluation team. She has served as a faculty associate at Arizona State Universitys School of Public Affairs, teaching applied statistics, and as a research assistant with the Arizona Alzheimers Disease Center/Banner Alzheimers Institute. Her research interests include statistical models for binary correlated data, Bayesian statistics, and their applications to public and behavioral health settings.

Dr. Ding-Geng Chen is a fellow of the American Statistical Association and the Wallace Kuralt distinguished professor at the University of North Carolina at Chapel Hill, as well as the South Africa DST-NRF-SAMRC,SARChI in Biostatistics (Tier 1). He was a professor in biostatistics at the University of Rochester and the Karl E. Peace endowed eminent scholar chair in biostatistics at Georgia Southern University. He is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in Monte-Carlo simulations, clinical trial biostatistics and public health statistics. Dr. Chen has more than 150 refereed publications. He has co-authored and co-edited 23 books on clinical trial methodology, meta-analysis and public health applications, and he has been invited nationally and internationally to give speeches on his research.


 

Schlagwörter zu:

Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates von Jeffrey R. Wilson - mit der ISBN: 9783030489045

Bayesian GMM estimators; GEE; GMM; GMM estimators; correlated observations; estimators; generalized estimating equation; generalized estimating equation estimators; generalized method of moments estimators; method of moments; population averaged; population-averaged; B; Public Health; Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Statistical Theory and Methods; Biostatistics; Health Care Management; Mathematics and Statistics, Online-Buchhandlung


 

Kunden Rezensionen: Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates | Buch oder eBook | Jeffrey R. Wilson

Zu diesem Artikel ist noch keine Rezension vorhanden.
Helfen sie anderen Besuchern und verfassen Sie selbst eine Rezension.


 

Kunden, die sich für: "Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates" von Jeffrey R. Wilson als Buch oder eBook

interessiert haben, schauten sich auch die folgenden Bücher & eBooks an: