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.
Author(s): Paul D Allison
Edition: 2
Publisher: SAS Publishing
Year: 2010
Language: English
Pages: 336
Tags: Библиотека;Компьютерная литература;SAS / JMP;
Contents......Page 4
What Is Survival Analysis?......Page 10
What is Survival Data?......Page 11
Why Use Survival Analysis?......Page 13
Approaches to Survival Analysis......Page 14
What You Need to Know......Page 15
Computing Notes......Page 16
Censoring......Page 18
Describing Survival Distributions......Page 24
Interpretations of the Hazard Function......Page 27
Some Simple Hazard Models......Page 29
The Origin of Time......Page 32
Data Structure......Page 35
Introduction......Page 38
The Kaplan-Meier Method......Page 39
Testing for Differences in Survivor Functions
......Page 47
The Life-Table Method
......Page 58
Life Tables from Grouped Data......Page 64
Testing for Effects of Covariates
......Page 68
Log Survival and Smoothed Hazard Plots......Page 73
Conclusion......Page 78
Introduction......Page 80
The Accelerated Failure Time Model......Page 81
Alternative Distributions......Page 86
Categorical Variables and the CLASS Statement......Page 96
Maximum Likelihood Estimation
......Page 98
Hypothesis Tests......Page 104
Goodness-of-Fit Tests with the Likelihood-Ratio Statistic
......Page 107
Graphical Methods for Evaluating Model Fit
......Page 109
Left Censoring and Interval Censoring......Page 112
Generating Predictions and Hazard Functions......Page 117
The Piecewise Exponential Model......Page 121
Bayesian Estimation and Testing......Page 126
Conclusion......Page 133
Introduction......Page 134
The Proportional Hazards Model......Page 135
Partial Likelihood......Page 137
Tied Data......Page 150
Time-Dependent Covariates
......Page 162
Cox Models with Nonproportional Hazards
......Page 181
Interactions with Time as Time-Dependent Covariates
......Page 186
Nonproportionality via Stratification
......Page 188
Left Truncation and Late Entry into the Risk Set......Page 192
Estimating Survivor Functions......Page 195
Testing Linear Hypotheses with CONTRAST or TEST Statements
......Page 201
Customized Hazard Ratios
......Page 204
Bayesian Estimation and Testing
......Page 206
Conclusion......Page 209
Introduction......Page 212
Type-Specific Hazards......Page 213
Time in Power for Leaders of Countries: Example......Page 216
Estimates and Tests without Covariates
......Page 217
Covariate Effects via Cox Models
......Page 222
Accelerated Failure Time Models
......Page 229
Alternative Approaches to Multiple Event Types......Page 236
Conclusion
......Page 241
Introduction
......Page 244
The Logit Model for Discrete Time......Page 245
The Complementary Log-Log Model for Continuous-Time Processes......Page 249
Data with Time-Dependent Covariates
......Page 252
Issues and Extensions
......Page 255
Conclusion......Page 264
Unobserved Heterogeneity
......Page 266
Repeated Events......Page 269
Generalized R2......Page 291
Sensitivity Analysis for Informative Censoring......Page 292
How to Choose a Method......Page 298
Conclusion......Page 301
The LIFEHAZ Macro......Page 302
The PREDICT Macro......Page 305
The MYEL Data Set: Myelomatosis Patients......Page 308
The RECID Data Set: Arrest Times for Released Prisoners
......Page 309
The STAN Data Set: Stanford Heart Transplant Patients
......Page 310
The ALCO Data Set: Survival of Cirrhosis Patients
......Page 311
The LEADERS Data Set: Time in Power for Leaders of Countries......Page 312
The RANK Data Set: Promotions in Rank for Biochemists......Page 313
The JOBMULT Data Set: REpeated Job Changes......Page 314
References......Page 316
Index......Page 322
