Multivariate Survival Analysis and Competing Risks (Chapman & Hall/CRC Texts in Statistical Science) - Tapa dura

"Dr. Crowder’s book provides a comprehensive overview of various distributions and models that have been proposed for multivariate survival data and competing risks data. The book is divided into four parts. ... The material discussed in each topic is succinct, which provides enough detail to follow and includes relevant references for interested audience to dig deeper. The accompanying real-world data examples and R code are extremely handy for anyone who would like to explore those methods. ... Despite the advanced topics it covers, the book is a pleasant read, as the presentation style is entertaining. ...
All together the book is very suitable for an advanced survival analysis course for its broad range of topics."
―Yu Cheng, University of Pittsburgh, in the LIDA-IG Newsletter, January 2018

"... the book is interesting to read, [and] the author’s writing style is both entertaining and to the point ..."
―ISCB News, December 2015

"... a nice addition to the previous edition is the inclusion of R code and datasets, which are available online. ... this book is a useful addition to the literature, which undergraduate as well as graduate students in statistics will appreciate."
―Australian & New Zealand Journal of Statistics, 56(4), 2014

"Crowder is known for his clear expositions and chatty style, and this book does not disappoint. It is a pleasant read. The introduction to R will be useful, as will the exercises at the end of each chapter. ... With its exercises and easy style, this book is very suitable as an upper-level text. It is easy to jump into later chapters without much back pedaling and this makes it a useful reference work."
―Roger M. Cooke, Journal of the American Statistical Association, September 2014, Vol. 109

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods.

There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.