1st Edition
The Statistical Analysis of Multivariate Failure Time Data A Marginal Modeling Approach
Introduction to Multivariate Failure Time Data. Bivariate Survivor Function Representation and Estimation. Regression Analysis of Bivariate Failure Time Data. Transformation Models, Frailties and Copulas for Bivariate Failure Time Regression. Regression Analysis of Higher Dimensional Failure Time Data. Recurrent Events and Life History Analysis. Missing and Mismeasured Data in Multivariate Failure Time Analysis. Other Failure Time Data Analysis Topics.
Biography
Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine.
Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.
"Here, Prentice (Univ. of Washington) and Zhao (National Inst. of Environmental Health Sciences) provide a systematic introduction to novel statistical methodology, using a “marginal modeling approach” relevant to a number of fields where interpretation of survival outcomes and failure over time data is required.The authors explore the entirety of each method covered, progressing from background mathematics to assumptions and caveats, and finally to interpretation. Intended for biostatistical researchers engaged in analysis of complex population data sets as encountered, for example, in randomized clinical trials, this volume may also serve as a reference for quantitative epidemiologists. Readers will need a solid understanding of statistical estimation methods and a reasonable command of calculus and probability theory. Appropriate exercises accompany each chapter, and links to software and sample data are provided (appendix B)."
~K. J. Whitehair, independent scholar, CHOICE, January 2020 Vol. 57 No. 5
Summing Up: Recommended. Graduate students, faculty and practitioners.
