Advanced Survival Models

Abstract

Presenting a thorough review of selected topics in survival analysis, Dr. Legrand’s Advanced Survival Models is an excellent reference for students and/or practitioners. The book covers four advanced topics: frailty models, cure models, competing risks, and joint modeling of time-dependent covariates. Each topic is addressed with great care, balancing coverage with intimate detail so that readers come away with a comfortable level of knowledge about each topic. The book is divided into six chapters. The first chapter covers basic survival analysis concepts and presents six medical datasets (many of which are publicly available) for illustrating the models going forward. Each dataset is explained in great detail, including the context of data collection and the meaning of each variable. The data are analyzed using primarily R code throughout with a sprinkling of SAS code as well. The second chapter then presents a brief review of classical survival analysis techniques. It is in this chapter that readers get a taste of the level of detail with which Dr. Legrand discusses each of the advanced models: parametric models, semi-parametric models, non-parametric models, Cox proportional hazards, accelerated failure time; all are given their due diligence and illustrated with the data provided, so that the reader is presented with the breadth of methodology available at even the basic level. For the remaining four chapters, the format is similar. The chapter opens with an overall introduction of the topic, effectively summarizing the contents to come. Next, the primary model varieties are presented with sufficient context to understand their origins as well as their areas of appropriate use. Next, the primary methods of estimating the models are discussed. Finally, the chapter ends with illustration of the models using one or more of the datasets. In each case, Dr. Legrand presents enough detail so that the reader becomes intimately familiar with the basic concepts and estimation procedures. As an illustration of her effectiveness, I was not aware of the existence of cure models prior to reading this book. Now, I feel confident enough on the subject that I would be comfortable explaining it to another person. Where there is the opportunity for more specialized models and/or estimation procedures, multiple references are provided that discuss such models and/or procedures in greater detail. I found the references satisfactory for further study on a particular topic. While the book overall is a fairly easy read, there are several editorial “glitches” that, though not sufficient to cause confusion and misunderstanding, were still noticeable and tended to happen more frequently than one would expect. Most of these “glitches” consist of typographical errors and awkward sentence structures. In addition, the material was a bit repetitive when moving from the introduction to the main material in each chapter. However, I consider both of these to be very minor inconveniences as neither detracted from my understanding of the material. In conclusion, I consider this book an invaluable reference for any practitioner or researcher in survival analysis. I could also see it as an excellent text for graduate students. I am very much grateful for Dr. Legrand’s work in bringing knowledge of these advanced models to the statistical community.