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Survival Analysis Using S
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INTRODUCTION
Motivation - Two Examples
Basic definitions
Censoring and Truncation Models
Course Objectives
Data entry and Import/Export of Data Files
Exercises
NONPARAMETRIC METHODS
Kaplan-Meier Estimator of Survival
Comparison of Survivor Curves: Two-Sample Problem
Exercises
PARAMETRIC METHODS
Frequently Used (Continuous) Models
Maximum Likelihood Estimation (MLE)
Confidence Intervals and Tests
One-Sample Problem
Two-Sample Problem
A Bivariate Version of the Delta Method
The Delta Method for a Bivariate Vector Field
General Version of the Likelihood Ratio Test
Exercises
REGRESSION MODELS
Exponential Regression Model
Weibull Regression Model
Cox Proportional Hazards (PH) Model
Accelerated Failure Time Model
Summary
AIC procedure for Variable Selection
Exercises
THE COX PROPORTIONAL HAZARDS MODEL
AIC Procedure for Variable Selection
Stratified Cox PH Regression
Exercises
Review of First Five Chapters: Self-Evaluation
MODEL CHECKING: DATA DIAGNOSTICS
Basic graphical Methods
Weibull Regression Model
Cox proportional Hazards Model
Exercises
Extended Cox Model
Competing Risks: Cumulative Incidence Estimator
Analysis of Left-Truncated and Right-Censored Data
Exercises
CENSORED REGRESSION QUANTILES, by Stephen Portnoy
Introduction
What are Regression Quantiles?
Computation of Censored Regression Quantiles
Examples of Censored Regression Quantile
Exercises
REFERENCES
INDEX

#### Reviews

"All in all the book succeeds nicely in getting the reader through the basic methods of survival analysis (Kaplan-Meier, log-rank, Weibull and Cox regression) and how to implement them in S."
-Journal of Statistical Software, Vol. 11, July 2004

"There are many books on survival analysis, so an obvious question is what makes this one any different ...? The main answers are the well-integrated S code that is used throughout the book and a chapter on censored regression quantiles ... . [T]he topics that are covered ... provide the reader with a good grasp of the principles of analysing survival data and the writing style is clear and easy to follow. I recommend this book for anyone who wants a good introduction to practical survival analysis using S."
-Journal of the Royal Statistics Society, Issue 167(4)

"This book introduces the field of survival analysis in a concise, coherent manner that capture the spirit of the methods without getting too embroiled in theoretical technicalities...this well-written book would be an excellent choice for a textbook for a course in survival analysis."
-Zentralblatt MATH 104

"This well-written book would be an excellent choice for a textbook for a course in survival analysis. All of the usual topics for a course in survival analysis are covered, including a careful discussion of parametric models. The explanations are clear and concise. The book not only teaches about the statistical methods for survival analysis, but also provides detailed instruction on how to do the computations with S-PLUS or R at a level where students will become proficient with the S language. The book contains an excellent collection of exercises. These exercises have been usefully partitioned into applications and questions that ask students to use their knowledge of probability and mathematical statistics."
-William Q Meeker, Distinguished Professor in the Department of Statistics, Iowa State University  