Modelling Survival Data in Medical Research

by David Collett

Critically acclaimed and resoundingly popular in its first edition, Modelling Survival Data in Medical Research has been thoroughly revised and updated to reflect the many developments and advances—particularly in software—made in the field over the last 10 years. Now, more than ever, it provides an outstanding text for upper-level and graduate courses in survival analysis, biostatistics, and time-to-event analysis.The treatment begins with an introduction to survival analysis and a description of four studies that lead to survival data. Subsequent chapters then use those data sets and others to illustrate the various analytical techniques applicable to such data, including the Cox regression model, the Weibull proportional hazards model, and others. This edition features a more detailed treatment of topics such as parametric models, accelerated failure time models, and analysis of interval-censored data. The author also focuses the software section on the use of SAS, summarising the methods used by the software to generate its output and examining that output in detail. Profusely illustrated with examples and written in the author's trademark, easy-to-follow style, Modelling Survival Data in Medical Research, Second Edition is a thorough, practical guide to survival analysis that reflects current statistical practices.

408 pages, Paperback

First published March 28, 2003

Reviews

[JuliAnna · Rating 4 out of 5]

A clear introduction for mathematically inclined applied statisticians, however I would not recommend it as an introduction for most researchers. For those researchers preferring a more accessible, hands-on approach I would recommend Allison's Survival Analysis Using SAS as an introductory text.

[Monika]

CWRU