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Survival Analysis Using SAS - A Practical Guide (Allison)

 
 Author(s)  Paul D. Allison
 Title  Survival Analysis Using SAS - A Practical Guide
 Edition  
 Year  1995
 Publisher  SAS Institute Inc.
 ISBN  1-55544-279
 Website  www.sas.com
 book link
 

Data and SAS code are available at:
  The example data listed in Appendix A is located at 
http://support.sas.com/publishing/bbu/zip/61339.zip





Table of Contents

Chapter 1 Introduction 1
  • What Is Survival Analysis?
  • What Is Survival Data?
  • Why Use Survival Analysis?
  • Approaches to Survival Analysis
  • What You Need to Know
  • Computing Notes

Chapter 2 Basic Concepts of Survival Analysis
  • Introduction
  • Censoring
  • Describing Survival Distributions
  • Interpretations of the Hazard Function
  • Some Simple Hazard Models
  • The Origin of Time
  • Data Structure

Chapter 3 Estimating and Comparing Survival Curves with PROC LIFETEST
  • Introduction
  • The Kaplan-Meier Method
  • Testing for Differences in Survivor Functions
  • The Life-Table Method
  • Life Tables from Grouped Data
  • Testing for Effects of Covariates
  • Log Survival and Smoothed Hazard Plots
  • Conclusion

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
  • Introduction
  • Type-Specific Hazards
  • Time in Power for Leaders of Countries: Example
  • Estimates and Tests without Covariates
  • Covariate Effects via Cox Models
  • Accelerated Failure Time Models
  • Alternative Approaches to Multiple Event Types
  • Conclusion

Chapter 7 Analysis of Tied or Discrete Data with PROC LOGISTIC
  • Introduction
  • The Logit Model for Discrete Time
  • The Complementary Log-Log Model for Continuous-Time Processes
  • Data with Time-Dependent Covariates
  • Issues and Extensions
  • Conclusion

Chapter 8 Heterogeneity, Repeated Events, and Other Topics
  • Introduction
  • Unobserved Heterogeneity
  • Repeated Events
  • Generalized R2
  • Sensitivity Analysis for Informative Censoring

Chapter 9 A Guide for the Perplexed
  • How to Choose a Method
  • Conclusion

Appendix 1 Macro Programs
  • Introduction
  • The LIFEHAZ Macro
  • The PREDICT Macro

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




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