http://www.crcpress.com/product/isbn/9781584884088

Summary
Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics.

The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s).

In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.


Table of Contents
Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics.

The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s).

In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.
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
ADDITIONAL TOPICS
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

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