This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area.
The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided.
Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions.
Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals. This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets.
Introduction Estimating the Survival and Hazard Functions The Cox Model Residuals Functional Form Testing Proportional Hazards Influence Multiple Events per Subject Frailty Models Expected Survival
'From the reviews:
TECHNOMETRICS"I would be curious to know how many people bought the book by Fleming and Harrington (1991) and by Anderson, Borgan, Gill, and Keiding (1993) when they first appeared hoping for an introduction to the subject. Instead we all should have saved our money and waited fir this volume by Therneau and Grambsch...This book can serve as a useful reference for statistical practitioners who encounter survival data and for researchers who want to update their knowledge in modern survival analysis...The writing style is light and almost humorous in many places. We get the feeling that the authors had a lot of fun writing this book. If only it was available a decade ago."STATISTICAL METHODS IN MEDICAL RESEARCH"In short, this is an exciting book, which introduces and illustrates some recent developments in surviavl analysis. The authors maintain an informal and good-humoures style, making the book very easy to read, and insist on a hands-on approach which encourages the reader to re-work examples. I would recommend it to any statistician analysing survival data."SHORT BOOK REVIEWS"The authors ... have laid out for us the wealth of their practical experience at all levels; the numerical aspects; computer algorithms; evaluation of different methods and connections between them; possible pitfalls; and interpretation of the results. Remarkable insights abound.
This book completes that of P. Hougaard by giving much detail on actual fitting of the models discussed by him.
It will serve two audiences: the busy practitioner who has not had time to catch up with martingale theory and counting processes and the graduate student who has just completed such a course and who needs to be introduced to the practicalities and judgements needed in data analysis. It is likely to become a well-thumbed copy on the statistician's desk and statistical practice will be the better for it."STATISTICS IN MEDICINE"I use S-PLUS in my own applied work and when testing my methodological research. Therefore, I came to this book with high expectations. I was not disappointed. The book is an invaluable resource for all researchers who use SAS and/or S-PLUS in their applied work, and who want to improve their skills in analyzing survival and event history data."JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION"...I highly recommend [this book] to statisticians analyzing survival data with S-PLUS and SAS.""Analogous development for Cox's regression model - stimulated partially by the counting process theory - has taken place over the last quarter of a century. This book provides a well-organized and extensive collection of these methods. ... The book is reasonably self-contained. It brings the fruits of the counting process-based methods to the common analyst ... . A number of biostatistical data sets have been used for the purpose of illustrative analysis. ... I think the book achieves more than its stated objective." (Debasis Sengupta, Sankhya, Vol. 65 (4), 2003)"This book models survival data, mainly in terms of the Cox regression model and its extensions ... . The text is fluently written in the style of a medium-level oral presentation which makes the book well readable and its contents well understandable ... . Difficult theoretical concepts are explained in an easy, yet instructive way ... . I consider this book as a most valuable source for beginners ... and I warmly recommend this book as introductory reading, guidance, and reference for practical work." (Jochen Mau, Metrika, February, 2003)"This book presents a state-of-the-art overview on modeling survival data. ... examples underline the enormous flexibility and potential of the discussed models. ... Appendices giving short tutorials into the statistical packages SAS and A-Plus as well as selected data sets will be very useful for most readers. ... The well chos
Introduction * Estimating the Survival and Hazard Functions * The Cox Model * Residuals * Functional Form * Testing Proportional Hazards * Influence * Multiple Events per Subject * Frailty Models * Expected Survival
This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets.
This book presents some of the recent developments in survival methods, along with the appropriate code in S-PLUS and SAS so they can be implemented. The book will be of interest to researchers, practitioners, and graduate students working in the areas of biostatistics, epidemilogy, and medical research.