» The Frailty Model (Statistics for Biology and Health)

The Frailty Model (Statistics for Biology and Health) Reviews

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Summary: first serious book dedicated to Frailty Models
Comment: The authors are academics who have done serious research in survival analysis and are very familiar with frailty models. The topic comes up when time to event data for one event is correlated with the time to event data for another or other events. This topic is sometimes referred to a subject in multivariate survival analysis or the analysis of clustered survival data.

As a professional biostatistician with a keen interest in survival models I have attended professional meetings in recent years and heard the term Frailty Model mentioned but I didn't know what it was. There of course is the natural connotation of weakness as in a feeble or frail person. But the actual formal dtatisticial meaning was a mystery. Other books that I am very familar with deal in part with frailty models but this is to my knowledge the first serious text dedicated to this topic. It also covers related methods to accomplish the same goal such as copulas (another term common in recent books and literature but one I was not familiar with either). For example Philip Hougaard wrote the first advanced text on multivariate survival models and covers parametric forms of frailty models. Klein and Moeschberger wrote a generaal survival analysis book that includes a chapter on semi-parameric fraility models. It showa how the EM algorithm is used to estimate parameters of the models. Ibrahim and colleague wrote a book on Bayesian methods in survival analysis and cover the Bayesian approach to both semi-parametric and parametric fraility models. Therneau and Grambsch wrote a recent book on the Cox proportional hazard model and its extensions. It included information on semi-parametric frailty models using the penalized partial likelihood approach to estimation.

This book is a well-written introduction to fraility models that includes all these methods provides real world examples and good explanations on how to interpret the results. The examples are illustrated using the freeware language R. This book could serve as either an undergraduate or graduate text in statistical methods and is a great reference for biostatisticians.

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