Survival Analysis Using the SAS System: A Practical Guide
CONTENTS
ACKNOWLEDGMENTS
Chapter 1 Introduction
- 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-Meir Method
- Testing for Differences in Survivor Functions
- The Life-Table Method
- Life Tables from Grouped Data
- Testing for the 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
- Go
odness-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
-
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-D
ependent Covariates
- Nonproportionality via Stratification
- Left Truncation and Late Entry into the Risk Set
- Estimating Survivor Functions
- Residuals and Influence Statistics
- Testing Linear Hypotheses with the TES
T Statement
- 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
-
An Alternative Approach to Multiple Event Types
- Conclusion
Chapter 7 Analysis of Tied or Discrete Data with the
- LOGISTIC, PROBIT, and GENMOD Procedures
- Introduction
- The Logit Model for Discrete Time
- The Complementary Log-Log Model for Continuous-Time Processes
- Data with Time-Dependent Covariates
- Issues and Exte
nsions
- Conclusion
Chapter 8 Heterogeneity, Repeated Events, and
- Other Topics
- Introduction
- Unobserved Heterogeneity
- Repeated Events
- Generalized R
- Sensitivity Analysis for Informative Censoring
Chapter 9 A Guide for the Perplexed
- How to Choose a Method
- Conclusion
Appendix 1 Macro Programs
Introduction
The SMOOTH Macro
The LIFEHAZ Macro
The PREDICT Macro
The WLW 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 Can
cer 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