About the Book
The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text.
Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice.
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.
Table of Contents:
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.
About the Author :
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.
Review :
"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. 5Summing Up: Recommended. Graduate students, faculty and practitioners.
"This book gives thorough coverage and rigorous discussion of statistical methods for the analysis of multivariate failure time data. The structure of the book has been thoughtfully planned and it is carefully and clearly written - it does a nice job of clearly introducing concepts and models, as well as describing nonparametric methods of estimation. For the core theme on the analysis of multiple failure times, it explores different approaches to estimation and inference, and critiques competing methods in terms of robustness and efficiency. Authoritative coverage of additional topics including recurrent event analysis, multistate modeling, dependent censoring, and others, ensures it will serve as an excellent reference for those with interest in life history analysis. Illustrative examples given in the chapters help make the issues and approaches for dealing with them tangible, while the exercises at the end of each chapter give readers an opportunity to gauge their understanding of the material. It will therefore also serve very nicely as a basis for a second graduate course on specialized topics of life history analysis."
~Richard Cook, University of Waterloo"Let me congratulate the authors with this impressive work…This book could be a textbook for an advanced masters or Ph.D level course for a degree in biostatistics and statistics…This book focusses on the case that we want to understand the association between covariate process and a multivariate survival outcome. It includes targeting the univariate conditional hazards as well as the multivariate hazards functions. Instead of targeting intensities that condition on the full observed history, it focusses on histories that exclude the failure time history, so that a change in the Z process represents a change in future multivariate survival experience. The book also reviews copula models, frailty models, and models of intensities of counting processes, beyond their marginal hazard modeling approach…An important strength is the illustrations with real world interesting data from the Women's Health Study. Another important strength is its overview of various competing approaches, making it comprehensive, beyond the presentation of the unique marginal modeling approach developed by the authors. ~Mark van der Laan, University of California, Berkeley
"I expect this book to be highly useful to: (i) researches dealing with developing statistical multivariate survival methods; (ii) teachers of advanced survival methods for graduate classes; and (iii) Biostatistics/statistics PhD students focusing in the area of multivariate survival analysis. I believe that the book would be very useful as a reference and as a textbook. I, personally, would definitely use it for both purposes.. There is a need for a well-organized book focusing mainly on the recent developments in this research area, that are not included in older books...The manuscript is technically correct, very clearly written, and it is a pleasure reading it." (
~Malka Gorfine, Tel Aviv University
"This well-written book offers the basics of innovative approach to analyse and interpret correlated failure times data . . . I enjoyed reading this book. I highly recommend this book to statistics researchers, graduate students, engineers, and computing professionals."
~ Ramalingam Shanmugam, Texas State University
"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. 5Summing Up: Recommended. Graduate students, faculty and practitioners.
"This book gives thorough coverage and rigorous discussion of statistical methods for the analysis of multivariate failure time data. The structure of the book has been thoughtfully planned and it is carefully and clearly written - it does a nice job of clearly introducing concepts and models, as well as describing nonparametric methods of estimation. For the core theme on the analysis of multiple failure times, it explores different approaches to estimation and inference, and critiques competing methods in terms of robustness and efficiency. Authoritative coverage of additional topics including recurrent event analysis, multistate modeling, dependent censoring, and others, ensures it will serve as an excellent reference for those with interest in life history analysis. Illustrative examples given in the chapters help make the issues and approaches for dealing with them tangible, while the exercises at the end of each chapter give readers an opportunity to gauge their understanding of the material. It will therefore also serve very nicely as a basis for a second graduate course on specialized topics of life history analysis."
~Richard Cook, University of Waterloo
"Let me congratulate the authors with this impressive work…This book could be a textbook for an advanced masters or Ph.D level course for a degree in biostatistics and statistics…This book focusses on the case that we want to understand the association between covariate process and a multivariate survival outcome. It includes targeting the univariate conditional hazards as well as the multivariate hazards functions. Instead of targeting intensities that condition on the full observed history, it focusses on histories that exclude the failure time history, so that a change in the Z process represents a change in future multivariate survival experience. The book also reviews copula models, frailty models, and models of intensities of counting processes, beyond their marginal hazard modeling approach…An important strength is the illustrations with real world interesting data from the Women's Health Study. Another important strength is its overview of various competing approaches, making it comprehensive, beyond the presentation of the unique marginal modeling approach developed by the authors. ~Mark van der Laan, University of California, Berkeley
"I expect this book to be highly useful to: (i) researches dealing with developing statistical multivariate survival methods; (ii) teachers of advanced survival methods for graduate classes; and (iii) Biostatistics/statistics PhD students focusing in the area of multivariate survival analysis. I believe that the book would be very useful as a reference and as a textbook. I, personally, would definitely use it for both purposes.. There is a need for a well-organized book focusing mainly on the recent developments in this research area, that are not included in older books...The manuscript is technically correct, very clearly written, and it is a pleasure reading it." (
~Malka Gorfine, Tel Aviv University
"This well-written book offers the basics of innovative approach to analyse and interpret correlated failure times data . . . I enjoyed reading this book. I highly recommend this book to statistics researchers, graduate students, engineers, and computing professionals."
~ Ramalingam Shanmugam, Texas State University
