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Survival Analysis Using SAS

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

  1. PREFACE vii
  2. Chapter 1 Introduction
    1. What Is Survival Analysis?
    2. What Is Survival Data?
    3. Why Use Survival Analysis?
    4. Approaches to Survival Analysis
    5. What You Need to Know
    6. Computing Notes
  3. Chapter 2 Basic Concepts of Survival Analysis
    1. Introduction
    2. Censoring
    3. Describing Survival Distributions
    4. Interpretations of the Hazard Function
    5. Some Simple Hazard Models
    6. The Origin of Time
    7. Data Structure
  4. Chapter 3 Estimating and Comparing Survival Curves with PROC LIFETEST
    1. Introduction
    2. The Kaplan-Meier Method
    3. Testing for Differences in Survivor Functions
    4. The Life-Table Method
    5. Life Tables from Grouped Data
    6. Testing for Effects of Covariates
    7. Log Survival and Smoothed Hazard Plots
    8. Conclusion
  5. Chapter 4 Estimating Parametric Regression Models with PROC LIFEREG
    1. Introduction
    2. The Accelerated Failure Time Model
    3. Alternative Distributions
    4. Categorical Variables and the CLASS Statement
    5. Maximum Likelihood Estimation
    6. Hypothesis Tests
    7. Goodness-of-Fit Tests with the Likelihood-Ratio Statistic
    8. Graphical Methods for Evaluating Model Fit
    9. Left Censoring and Interval Censoring
    10. Generating Predictions and Hazard Functions
    11. The Piecewise Exponential Model
    12. Bayesian Estimation and Testing
    13. Conclusion
  6. Chapter 5 Estimating Cox Regression Models with PROC PHREG
    1. Introduction
    2. The Proportional Hazards Model
    3. Partial Likelihood
    4. Tied Data
    5. Time-Dependent Covariates
    6. Cox Models with Nonproportional Hazards
    7. Interactions with Time as Time-Dependent Covariates
    8. Nonproportionality via Stratification
    9. Left Truncation and Late Entry into the Risk Set
    10. Estimating Survivor Functions
    11. Testing Linear Hypotheses with CONTRAST or TEST Statements
    12. Customized Hazard Ratios
    13. Bayesian Estimation and Testing
    14. Conclusion
  7. Chapter 6 Competing Risks
    1. Introduction
    2. Type-Specific Hazards
    3. Time in Power for Leaders of Countries: Example
    4. Estimates and Tests without Covariates
    5. Covariate Effects via Cox Models
    6. Accelerated Failure Time Models
    7. Alternative Approaches to Multiple Event Types
    8. Conclusion
  8. Chapter 7 Analysis of Tied or Discrete Data with PROC LOGISTIC
    1. Introduction
    2. The Logit Model for Discrete Time
    3. The Complementary Log-Log Model for Continuous-Time Processes
    4. Data with Time-Dependent Covariates
    5. Issues and Extensions
    6. Conclusion
  9. Chapter 8 Heterogeneity, Repeated Events, and Other Topics
    1. Introduction
    2. Unobserved Heterogeneity
    3. Repeated Events
    4. Generalized R2
    5. Sensitivity Analysis for Informative Censoring
  10. Chapter 9 A Guide for the Perplexed
    1. How to Choose a Method
    2. Conclusion
  11. Appendix 1 Macro Programs
    1. Introduction
    2. The LIFEHAZ Macro
    3. The PREDICT Macro
  12. Appendix 2 Data Sets
    1. Introduction
    2. The MYEL Data Set: Myelomatosis Patients
    3. The RECID Data Set: Arrest Times for Released Prisoners
    4. The STAN Data Set: Stanford Heart Transplant Patients
    5. The BREAST Data Set: Survival Data for Breast Cancer Patients
    6. The JOBDUR Data Set: Durations of Jobs
    7. The ALCO Data Set: Survival of Cirrhosis Patients
    8. The LEADERS Data Set: Time in Power for Leaders of Countries
    9. The RANK Data Set: Promotions in Rank for Biochemists
    10. The JOBMULT Data Set: Repeated Job Changes
  13. References
  14. Index 313