1st Edition

Survival Analysis Using S Analysis of Time-to-Event Data

By Jong Sung Kim, Mara Tableman Copyright 2003
    280 Pages 69 B/W Illustrations
    by Chapman & Hall

    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
    "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