Scheike, Thomas H.

Journal of Statistical Software

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Thomas H. Scheike

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Survival Analysis using S

JSS Journal of Statistical SoftwareJuly 2004, Volume 11, Book Review 7. http://www.jstatsoft.org/Reviewer: Thomas H. ScheikeUniversity of CopenhagenSurvival Analysis Using SMara Tableman and Jong Sung KimChapman & Hall/CRC, Boca Raton, Florida, 2004.ISBN 1-58488-408-8. xv + 260 pp. $69.95.This book is aimed at students having their first encounter with survival analysis. Thestudents that the authors have in mind are not particularly mathematically oriented and theauthors aim primarily at introducing the basic concepts and showing how to implement themethods using S. The book contains 8 chapters that I will comment on briefly in the following.Chapter 1 introduces the reader to the special methods that are needed to deal with censoringby considering some examples.Chapter 2 introduces the standard non-parametric methods and contains material on theKaplan-Meier, the log-rank test and some stratified methods.Chapter 3 discusses parametric methods and the maximum likelihood principle.Chapter 4 introduces the reader to regression models. The chapter starts off with the ex-ponential and Weibull regression model and moves on to a brief introduction of the Coxregression model and the accelerated failure time model. The material in these sections doesnot contain a strong applied component but focuses more on the underlying formulae. Thefinal section of this chapter introduces the reader to Akaike’s information criterion (AIC) andshows how it can be used for comparing the parametric models of the chapter using S.Chapter 5 shows how the Cox regression model may be applied to real data using S. One issuethat takes up a lot of space is how to use the AIC for automated model selection proceduresin S. In biostatistics the search for models will often be guided by subject matter rather thanautomatic model selection procedures, and then one will investigate specific interactions tovalidate a simple additive structure among covariates. One section introduces the stratifiedCox regression model and shows how it can be used to validate the assumption of constantrelative risk over time. Everybody who has tried using these graphical tests will know howdifficult it is both in theory and practice to decide if the curves are parallel (on the appropriatescale), and this is perhaps why the authors only have a brief remark saying that the modelappears to fit.Chapter 6 contains some general material on model checking. It introduces Cox’s partiallikelihood and the various residuals used in survival analysis. S programs illustrate how these2 Survival Analysis Using Sresiduals may be used check the fit of Cox’s regression model. One final section discusseshow to find a maximum likelihood cut point for the Cox model using the profile likelihoodprinciple and suggests a bootstrap to asses the uncertainty.Chapter 7 moves on to consider the proportional hazards model with time-dependent co-variates and shows how to fit the models in S (S makes it possible to deal with piecewiseconstant covariates). Additional topics covered in this chapter are competing risks modelsand left-truncation.Chapter 8 is rather atypical for a first text on survival analyses and contains some new materialon censored regression quantiles. This is a nice twist to the book. I think the authors do agood job of pointing out that other methods are needed when the Cox regression model failsto fit the data. Even when the Cox model fits it is often very informative to get hold of othersummary statistics than those obtained from the Cox model. Regression quantiles providesuch an alternative methodology. I fear that this chapter will be very hard to read for theintended readers, especially because various issues are not quite settled yet. As in previouschapters the methods are implemented in S.The lacking formulae and mathematics underlying the different analyses makes it hard forthe reader to understand all issues in depth, I had quite a number of additional why ’s, butI am quite sure that the students will be able to do practical analyses on the computer andget the flavour of things.All in all the book succeeds nicely in getting the reader through the basic methods of survivalanalysis (Kaplan-Meier, log-rank, Weibull and Cox regression) and how to implement themin S.Reviewer:Thomas H. ScheikeUniversity of CopenhagenDepartment of BiostatisticsBlegdamsvej 3, DK-2200 Copenhagen NTelephone: +45/3532-7928Fax: +45/3532-7907E-mail: T.Scheike@biostat.ku.dkJournal of Statistical Software Published: 2004-07-26July 2004, Volume 11, Book Review 7.http://www.jstatsoft.org/

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Survival Analysis using S

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