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E-raamat: Survival Analysis Using SAS: A Practical Guide, Second Edition

  • Formaat: 336 pages
  • Ilmumisaeg: 29-Mar-2010
  • Kirjastus: SAS Institute
  • Keel: eng
  • ISBN-13: 9781629590257
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  • Formaat: 336 pages
  • Ilmumisaeg: 29-Mar-2010
  • Kirjastus: SAS Institute
  • Keel: eng
  • ISBN-13: 9781629590257
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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.
Preface vii
Chapter 1 Introduction
1(8)
What Is Survival Analysis?
1(1)
What Is Survival Data?
2(2)
Why Use Survival Analysis?
4(1)
Approaches to Survival Analysis
5(1)
What You Need to Know
6(1)
Computing Notes
7(2)
Chapter 2 Basic Concepts of Survival Analysis
9(20)
Introduction
9(1)
Censoring
9(6)
Describing Survival Distributions
15(3)
Interpretations of the Hazard Function
18(2)
Some Simple Hazard Models
20(3)
The Origin ofTime
23(3)
Data Structure
26(3)
Chapter 3 Estimating and Comparing Survival Curves with PROCLIFETEST
29(42)
Introduction
29(1)
The Kaplan-Meier Method
30(8)
Testing for Differences in Survivor Functions
38(11)
The Life-Table Method
49(6)
Life Tables from Grouped Data
55(4)
Testing for Effects of Covariates
59(5)
Log Survival and Smoothed Hazard Plots
64(5)
Conclusion
69(2)
Chapter 4 Estimating Parametric Regression Models with PROC LIFEREG
71(54)
Introduction
71(1)
The Accelerated Failure Time Model
72(5)
Alternative Distributions
77(10)
Categorical Variables and the CLASS Statement
87(2)
Maximum Likelihood Estimation
89(6)
Hypothesis Tests
95(3)
Goodness-of-Fit Tests with the Likelihood-Ratio Statistic
98(2)
Graphical Methods for Evaluating Model Fit
100(3)
Left Censoring and Interval Censoring
103(5)
Generating Predictions and Hazard Functions
108(4)
The Piecewise Exponential Model
112(5)
Bayesian Estimation and Testing
117(7)
Conclusion
124(1)
Chapter 5 Estimating Cox Regression Models with PROC PHREG
125(78)
Introduction
125(1)
The Proportional Hazards Model
126(2)
Partial Likelihood
128(14)
Tied Data
142(11)
Time-Dependent Covariates
153(19)
Cox Models with Nonproportional Hazards
172(5)
Interactions with Time as Time-Dependent Covariates
177(2)
Nonproportionality via Stratification
179(4)
Left Truncation and Late Entry into the Risk Set
183(3)
Estimating Survivor Functions
186(6)
Testing Linear Hypotheses with CONTRAST or TEST Statements
192(3)
Customized Hazard Ratios
195(2)
Bayesian Estimation and Testing
197(3)
Conclusion
200(3)
Chapter 6 Competing Risks
203(32)
Introduction
203(1)
Type-Specific Hazards
204(3)
Time in Power for Leaders of Countries: Example
207(1)
Estimates and Tests without Covariates
208(5)
Covariate Effects via Cox Models
213(7)
Accelerated Failure Time Models
220(7)
Alternative Approaches to Multiple Event Types
227(5)
Conclusion
232(3)
Chapter 7 Analysis of Tied or Discrete Data with PROC LOGISTIC
235(22)
Introduction
235(1)
The Logit Model for Discrete Time
236(4)
The Complementary Log-Log Model for Continuous-Time Processes
240(3)
Data with Time-Dependent Covariates
243(3)
Issues and Extensions
246(9)
Conclusion
255(2)
Chapter 8 Heterogeneity, Repeated Events, and Other Topics
257(32)
Introduction
257(1)
Unobserved Heterogeneity
257(3)
Repeated Events
260(22)
Generalized R2
282(1)
Sensitivity Analysis for Informative Censoring
283(6)
Chapter 9 A Guide for the Perplexed
289(4)
How to Choose a Method
289(3)
Conclusion
292(1)
Appendix 1 Macro Programs
293(6)
Introduction
293(1)
The LIFEHAZ Macro
293(3)
The PREDICT Macro
296(3)
Appendix 2 Data Sets
299(8)
Introduction
299(1)
The MYEL Data Set: Myelomatosis Patients
299(1)
The RECID Data Set: Arrest Times for Released Prisoners
300(1)
The STAN Data Set: Stanford Heart Transplant Patients
301(1)
The BREAST Data Set: Survival Data for Breast Cancer Patients
302(1)
The JOBDUR Data Set: Durations of fobs
302(1)
The ALCO Data Set: Survival of Cirrhosis Patients
302(1)
The LEADERS Data Set: Time in Power for Leaders of Countries
303(1)
The RANK Data Set: Promotions in Rank for Biochemists
304(1)
The JOBMULT Data Set: Repeated fob Changes
305(2)
References 307(6)
Index 313