Survival Analysis Using SAS: A Practical Guide
Written to presume only a basic knowledge of regression analysis and linear models, this is marvelously fluent and presents the survival-analysis material in an enjoyable and readable style. Some of the indigenous topics-such as competing risks, repeated events, multiple events, and event history-receive more emphasis in this book than in most other survival-analysis books. The book incidentally does a nice job of explaining the features in SAS that differentiate SAS survival-analysis capabilities from the capabilities of other (unnamed) survival-analysis software. --Eric R. Ziegel, Technometrics, Book Review EditorA very informative and practical text for statisticians and applied researchers interested in analyzing time-to-event data. The chapters are concise, well written and packed with useful real life examples. The underlying theory is clearly presented and supplemented with numerous diagrams and tables. Basic survival concepts and methods, such as parametric regression modeling, Kaplan-Meier analysis, and Cox regression are superbly covered. Yet, the text does not shy away from more advanced topics including accelerated failure-time model, time-dependent covariates, and competing risk models. --Jimmy Thomas Efird, Roche Global DevelopmentAllison has put together an excellent resource for survival analysis for the novice, researchers with a limited knowledge of statistics and those with extensive knowledge of statistics. His book contains nine chapters. The first two are introductory information on survival analysis including what survival analysis is, when and why one would use this technique, and approaches to survival analysis including life tables, Kaplan-Meier (KM), exponential regression, and proportional hazard regression. A description of probability functions, including the hazard function are explained in a manner that is easily understood. Chapter 3 explains using the procedure PROC LIFETEST, which produces estimates of survival functions using life tables or KM. Examples and sample data sets are provided for this procedure, and others that follow. Chapter 4 explains the procedure PROC LIFEREG, which produces estimates of parametric regression models with censored survival data using the method of maximum likelihood. Chapter 5 explains use of the procedure PROC PHREG which is proportional hazard modeling or the cox regression model. This procedure is fairly new, and only recently has been included in the core SAS STAT modules. The proportional hazard model has many applications due to the non-parametric nature of the model. Unlike PROC LIFEREG, PHREG does not depend on choosing a particular probability distribution to represent survival times. Allison's description of the model and the syntax for SAS is very easy to follow. Chapter 6 explains concept of competing risks and how to deal with them in the procedures already explained. Chapter 7 explains using PROC LOGISTIC, PROBIT and GENMOD when a survival history is broken down into a set of discrete observations. This chapter provides a quick summary of logistic regression models using SAS, and users with an advance need in this area should use the book Logistic Regression Examples which contains much more detail. Chapter 8 includes advance --Patrick J. Roohan, NYS Department of Health