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See References in the LIFEREG, LIFETEST, and PHREG chapters of the SAS/STAT User's Guide and in the RELIABILITY chapter of the SAS/QC User's Guide.

References from SAS

• Allison, Paul D. 1995. Survival Analysis Using the SAS System: A Practical Guide. Cary, NC: SAS Institute Inc.

  Comments (from the link above): Biomedical and social science researchers who want to analyze survival data with SAS will find just what they need with this easy-to-read and comprehensive guide. Written for the reader with a modest statistical background and minimal knowledge of SAS software, this book teaches many aspects of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, ensuring that even the uninitiated becomes a sophisticated user 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, such as time-dependent covariates, competing risks, and repeated events.

  Data sets and SAS code are available for this book from the link above.

• Allison, Paul D. 1999. Logistic Regression Using the SAS System: Theory and Application. Cary, NC: SAS Institute Inc.

  Comments: Included in this list of references for analysis of the discrete-choice model and matched case-control data that can be dealt with in the PHREG procedure.

  (from the link above): If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, this book is for you! Informal and nontechnical, this book both explains the theory behind logistic regression and looks at all the practical details involved in its implementation using SAS. Several social science real-world examples are included in full detail. The book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logit analysis, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis with the PHREG procedure, and Poisson regression. Other highlights include discussions of how to use the GENMOD procedure to do log-linear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed.

  Data sets and SAS code are available for this book from the link above.

• Cantor, Alan. B. 2003. SAS Survival Analysis Techniques for Medical Research, Second Edition. Cary, NC: SAS Institute Inc.

  Comments (from the link above): If you are new to survival analysis or want to expand your capabilities in this area, you'll benefit from Alan Cantor's follow-up to Extending SAS Survival Analysis Techniques for Medical Research. This second edition presents the theory and methods of survival analysis along with excellent discussions of the SAS procedures used to implement the methods described. New features include a discussion of permutation and randomization tests; a discussion of the use of data imputation; an expanded discussion of power for Cox regression; descriptions of the new features of SAS 9, such as confidence bands for the Kaplan-Meier curve; appendixes that cover mathematical and statistical background topics needed in survival analysis; and student exercises. The new features, along with several useful macros and numerous examples, make this a suitable textbook for a course in survival analysis for biostatistics majors and majors in related fields. This book excels at presenting complex ideas in a way that enables those without a strong technical background to understand and apply the concepts and techniques.

  Data sets and SAS code are available for this book from the link above.

• Stokes, Maura E., Charles S. Davis, and Gary G. Koch. 2000. Categorical Data Analysis Using the SAS System, Second Edition. Cary, NC: SAS Institute Inc.

  Comments: Included in this list of references for analysis of matched case-control data that can be dealt with in the PHREG procedure.

  (from the link above): Statisticians and researchers will find this book to be a useful discussion of categorical data analysis techniques as well as an invaluable aid in applying these methods with SAS. Practical examples from a broad range of applications illustrate the use of the FREQ, LOGISTIC, GENMOD, and CATMOD procedures in a variety of analyses. Other procedures discussed include the PHREG and NPAR1WAY procedures. Topics discussed include assessing association in contingency tables and sets of tables, logistic regression and conditional logistic regression, weighted least squares modeling, repeated measurements analyses, log-linear models, and bioassay analysis. The second edition has been revised for use with Version 8 of SAS. New topics include additional exact tests, generalized estimating equations, use of the CLASS statement in the LOGISTIC procedure, exact logistic regression using the LOGISTIC procedure, and comparisons of the use of subject-specific models versus population-averaged models. This title is also available in digital format.

  Data sets and SAS code are available for this book from the link above.

General References Not from SAS

• Collet, D. 1994. Modelling Survival Data in Medical Research. London: Chapman & Hall. ISBN: 0-412-44880-7 (cloth), 0-412-44890-4 (paperback)

  Comments: A very readable book with discussions of methods available in the LIFEREG, LIFETEST, and PHREG procedures. The author provides a short discussion of each procedure at the back of the book and annotated output from an invocation of each procedure. The example data sets are from medical situations. The book contains a short but informative chapter on time-dependent covariates. Presents practical insights on model selection with criticism of automatic variable selection procedures.

• Greene, William H. 2000. Econometric Analysis. 4th ed. Prentice-Hall, Upper Saddle River, NJ. ISBN: 0-13-013297-7

  Comments: Included here because the author discusses the Tobit model, which can be fit in the LIFEREG procedure. The author shows transformations that are necessary to produce predicted values for the Tobit model in PROC LIFEREG that are used in a LIFEREG documentation example. Contains a short treatment of marginal effects in the Tobit model and a brief discussion of multinomial logit and conditional logit models.

• Hosmer, David W., Jr., and Stanley Lemeshow. 1999. Applied Survival Analysis: Regression Modeling of Time to Event Data. John Wiley & Sons, New York. ISBN: 0-471-15410-5

  Comments: An excellent applied book with good practical information, explanations, and suggestions. One chapter covers methods that are offered in PROC LIFETEST, but the book is devoted primarily to Cox regression, which is available in PROC PHREG. Chapters on interpretations, model development, model adequacy, and the stratified Cox model. One chapter is on parametric regression models, which can be fit in PROC LIFEREG. The concluding chapter discusses additional topics such as recurrent events, frailty models, and additive models.

• Klein, John P., and Melvin L. Moeschberger. 1997. Survival Analysis: Techniques for Censored and Truncated Data. Springer-Verlag, New York. ISBN: 0-387-94829-5

  Comments: A very comprehensive reference for formulas and explanation of methods used in the LIFEREG, LIFETEST, and PHREG procedures. It covers five major themes: "basic concepts and terminology, estimation of summary survival statistics based on censored and/or truncated data, hypothesis testing, regression analysis for censored and/or truncated data, and multivariate models for survival data." The book has good practical and theoretical notes at the end of each chapter. An excellent reference text with numerous real data sets used throughout the book for illustration.

• Kalbfleisch, John D., and Ross L. Prentice. 2002. The Statistical Analysis of Failure Time Data, Second Edition. John Wiley & Sons, New York. ISBN: 0-471-36357-X

  Comments: A classic reference text with a unified theoretical treatment of many survival analysis fundamentals in an updated and improved second edition. Eleven chapters: Introduction; Failure Time Models; Inference in Parametric Models and Related Topics; Relative Risk (Cox) Regression Models; Counting Processes and Asymptotic Theory; Likelihood Construction and Further Results; Rank Regression and the Accelerated Failure Time Model; Competing Risks and Multistate Models; Modeling and Analysis of Recurrent Event Data; Analysis of Correlated Failure Time Data; Additional Failure Time Data Topics, plus appendixes with data sets and supporting technical material.

• Kleinbaum, David G. 1996. Survival Analysis: A Self-Learning Text. Springer-Verlag, New York. ISBN: 0-387-94543-1

  Comments: Written as a self-study text at an approachable level, it is easy to read and comprehend. Has good worked-out examples with explanations of basic formulas and concepts that are used in PROC LIFETEST and PROC PHREG. Six chapters: Introduction to Survival Analysis; Kaplan-Meier Survival Curves and the Log-Rank Test; The Cox Proportional Hazards Model and Its Characteristics; Evaluating the Proportional Hazards Assumption; The Stratified Cox Procedure; Extension of the Cox Proportional Hazards Model for Time-Dependent Variables.

• Lawless, Jerald F. 1982. Statistical Models and Methods for Lifetime Data. John Wiley & Sons, New York. ISBN: 0-471-08544-8

  Comments: Broad treatment of methods contained in the LIFEREG, LIFETEST, and PHREG procedures with more emphasis on methods that are applicable to PROC LIFEREG including treatment of the generalized gamma distribution. Ten chapters: Basic Concepts and Models; Life Tables, Graphs, and Related Procedures; Inference Procedures for Exponential Distributions; Inference Procedures for Weibull and Extreme Value Distributions; Inference Procedures for Some Other Models; Parametric Regression Models; Distribution-Free Methods for the Proportion Hazards and Related Regression Models; Nonparametric and Distribution-Free Methods; Goodness of Fit Tests; Multivariate and Stochastic Process Models.

• Meeker, William Q., and Luis A. Escobar. 1998. Statistical Methods for Reliability Data. John Wiley & Sons, New York. ISBN: 0-471-14328-6

  Comments: An up-to-date and practical treatment of reliability data methods; an outstanding reference book for formulas, distributions, and methods primarily applicable to PROC LIFEREG and PROC RELIABILITY. Examples focus on "reliability or durability of materials, components, and relatively simple systems." Interesting epilogue with advice and insights into real world challenges. Numerous real data sets in Appendix C.

• Nelson, Wayne. 1982. Applied Life Data Analysis. John Wiley & Sons, New York. ISBN: 0-471-09458-7

  Comments: Has many engineering examples and gives copious information on plotting and modeling of reliability data. Pre-dates the development of the RELIABILITY procedure in SAS. Contains formulas that are applicable to binomial and Poisson analyses in PROC RELIABILITY.

• Therneau, Terry M., and Patricia M. Grambsch. 2000. Modeling Survival Data: Extending the Cox Model. Springer-Verlag, New York. ISBN: 0-387-98784-3

  Comments: This book discusses moving beyond traditional techniques of survival analysis. From the book: "Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards, and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scale, smoothing and regression splines, and the computation of expected survival curves." Ten chapters: Introduction; Estimating the Survival and Hazard Functions; The Cox Model; Residuals; Functional Form; Testing Proportional Hazards; Influence; Multiple Events per Subject; Frailty Models and Expected Survival. Appendixes on introductions to SAS and S-Plus, author-written SAS macros and S functions, and some of the real data sets used in the book.

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