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Survival Analysis Using SAS: A Practical Guide, Second Edition
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About Survival Analysis Using SAS: A Practical
Product Description Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. Although the book assumes only a minimal knowledge of SAS, more experienced users will learn new techniques of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, ensuring that even the uninitiated become sophisticated users of survival analysis. The main topics presented include censoring, survival curves, Kaplan-Meier estimation, accelerated failure time models, Cox regression models, and discrete-time analysis. Also included are topics not usually covered in survival analysis books, such as time-dependent covariates, competing risks, and repeated events. Survival Analysis Using SAS: A Practical Guide, Second Edition, has been thoroughly updated for SAS 9, and all figures are presented using ODS graphics. This new edition also documents major enhancements to the STRATA statement in the LIFETEST procedure; includes a section on the PROBPLOT command, which offers graphical methods to evaluate the fit of each parametric regression model; introduces the new BAYES statement for both parametric and Cox models, which allows the user to do a Bayesian analysis using MCMC methods; demonstrates the use of the counting process syntax as an alternative method for handling time-dependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate random-effects models for discrete-time data. Review "Survival Analysis Using SAS: A Practical Guide, Second Edition, is a prime but by no means the only example of Paul Allison's skill as a writer and teacher. Allison has a perhaps unparalleled ability to write about highly complex topics in a way that is accessible to relatively inexperienced people at the same time that he provides fresh insights and explanations to practitioners who may have thought they knew all there was to know. His writing reflects not only his deep knowledge of statistical methods but also his substantive engagement with them as a first-rate sociologist. Allison writes with the focus and confidence of someone who knows what he is doing and why he is doing it. His discussion of the basics of survival analysis is as clear as one can make it, but he does not gloss over the underlying mathematics, providing extraordinarily clear and detailed discussions of maximum and partial likelihood, Baysian estimation methods, and other topics that are essential to a thorough understanding of the methods. The examples, all of them based on real data, are instructive and thoroughly explained. An important aspect of the examples is that preliminary SAS code needed to arrange the data for analysis is carefully discussed, thus making the book more accessible to those who are new to SAS. For SAS users, I can think of no better place to start one's education regarding survival models, and I would urge anyone who already uses them to give this book a careful read." --Richard T. Campbell, Professor of Biostatistics and Sociology, University of Illinois at Chicago "A complete novice to this subject, I learned survival analysis on the fly from a client who had the utmost confidence in my ability. I practiced due diligence by obsessively double-checking my code and methods against a hodge-podge of references. However, published information was so lacking in substance, consistency, and applicability that I asked her how to set up the censoring variable and produced what is obviously in hindsight an alarming number of staged 2xn frequency tables for quality assurance purposes. With Survival Analysis Using
