Book 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.
This book is part of the SAS Press program.
Table of contents
- PREFACE vii
- Chapter 1 Introduction
- Chapter 2 Basic Concepts of Survival Analysis
- Chapter 3 Estimating and Comparing Survival Curves with PROC LIFETEST
-
Chapter 4 Estimating Parametric Regression Models with PROC LIFEREG
- Introduction
- The Accelerated Failure Time Model
- Alternative Distributions
- Categorical Variables and the CLASS Statement
- Maximum Likelihood Estimation
- Hypothesis Tests
- Goodness-of-Fit Tests with the Likelihood-Ratio Statistic
- Graphical Methods for Evaluating Model Fit
- Left Censoring and Interval Censoring
- Generating Predictions and Hazard Functions
- The Piecewise Exponential Model
- Bayesian Estimation and Testing
- Conclusion
-
Chapter 5 Estimating Cox Regression Models with PROC PHREG
- Introduction
- The Proportional Hazards Model
- Partial Likelihood
- Tied Data
- Time-Dependent Covariates
- Cox Models with Nonproportional Hazards
- Interactions with Time as Time-Dependent Covariates
- Nonproportionality via Stratification
- Left Truncation and Late Entry into the Risk Set
- Estimating Survivor Functions
- Testing Linear Hypotheses with CONTRAST or TEST Statements
- Customized Hazard Ratios
- Bayesian Estimation and Testing
- Conclusion
- Chapter 6 Competing Risks
- Chapter 7 Analysis of Tied or Discrete Data with PROC LOGISTIC
- Chapter 8 Heterogeneity, Repeated Events, and Other Topics
- Chapter 9 A Guide for the Perplexed
- Appendix 1 Macro Programs
-
Appendix 2 Data Sets
- Introduction
- The MYEL Data Set: Myelomatosis Patients
- The RECID Data Set: Arrest Times for Released Prisoners
- The STAN Data Set: Stanford Heart Transplant Patients
- The BREAST Data Set: Survival Data for Breast Cancer Patients
- The JOBDUR Data Set: Durations of Jobs
- The ALCO Data Set: Survival of Cirrhosis Patients
- The LEADERS Data Set: Time in Power for Leaders of Countries
- The RANK Data Set: Promotions in Rank for Biochemists
- The JOBMULT Data Set: Repeated Job Changes
- References
- Index 313
Product information
- Title: Survival Analysis Using SAS
- Author(s):
- Release date: March 2010
- Publisher(s): SAS Institute
- ISBN: 9781599948843
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