The statistical analysis of multivariate failure time data: a marginal modeling approach/ Ross L Prentice and Shanshan Zhao

Includes bibliographical references and index

1. Introduction and characterization of multivariate failure time distributions -- 2. Univariate failure time data analysis methods -- 3. Nonparametric estimation of the bivariate survivor function -- 4. Regression analysis of bivariate failure time data -- 5. Trivariate failure time data modeling and analysis -- 6. Higher dimensional failure time data modeling and estimation -- 7. Recurrent event data analysis methods -- 8. Additional important multivariate failure time topics -- Glossary of notation -- Appendix A: Technical materials -- Appendix B: Software and data -- Bibliography -- Author index -- Subject index

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.