Survival Analysis Using S

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.

Contents

• 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