Fills the need for an intermediate textbook in survival analysis Introduces censored regression quantiles for the first time, in a chapter written by Stephen Portnoy of the University of IllinoisIntroduces the bootstrap validation of cut point analysisSuccinctly discusses competing risks and the necessity of the cumulative incidence estimatorSkillfully weaves S and R into the text, making the book suitable for class use, as a self-learning text, and as a WebCT course Offers a lecture-book format that presents a list of objectives at the beginning of each chapter and a summary of results for each analysis Provides web support at www.crcpress.com/e_products/downloads/ containing author-written S-functions and data sets used in the bookProvides a solutions manual with qualifying course adoptions
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
Motivation - Two Examples
Censoring and Truncation Models
Data entry and Import/Export of Data Files
Kaplan-Meier Estimator of Survival
Comparison of Survivor Curves: Two-Sample Problem
Frequently Used (Continuous) Models
Maximum Likelihood Estimation (MLE)
Confidence Intervals and Tests
A Bivariate Version of the Delta Method
The Delta Method for a Bivariate Vector Field
General Version of the Likelihood Ratio Test
Exponential Regression Model
Weibull Regression Model
Cox Proportional Hazards (PH) Model
Accelerated Failure Time Model
AIC procedure for Variable Selection
THE COX PROPORTIONAL HAZARDS MODEL
AIC Procedure for Variable Selection
Stratified Cox PH Regression
Review of First Five Chapters: Self-Evaluation
MODEL CHECKING: DATA DIAGNOSTICS
Basic graphical Methods
Weibull Regression Model
Cox proportional Hazards Model
Extended Cox Model
Competing Risks: Cumulative Incidence Estimator
Analysis of Left-Truncated and Right-Censored Data
CENSORED REGRESSION QUANTILES, by Stephen Portnoy
What are Regression Quantiles?
Computation of Censored Regression Quantiles
Examples of Censored Regression Quantile
"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