Modelling Survival Data in Medical Research (Chapman & Hall/CRC Texts in Statistical Science), 4th Edition (PDF)

By David Collett

Modelling Survival Data in Medical Research, Fourth Edition, describes the analysis of survival data, illustrated using a wide range of examples from biomedical research. Written in a non-technical style, it concentrates on how the techniques are used in practice. Starting with standard methods for summarizing survival data, Cox regression and parametric modeling, the book covers many more advanced techniques, including interval-censoring, frailty modeling, competing risks, analysis of multiple events, and dependent censoring.

This new edition contains chapters on Bayesian survival analysis and use of the R software. Earlier chapters have been extensively revised and expanded to add new material on several topics. These include methods for assessing the predictive ability of a model, joint models for longitudinal and survival data, and modern methods for the analysis of interval-censored survival data.

Features:

• Presents an accessible account of a wide range of statistical methods for analyzing survival data
• Contains practical guidance on modeling survival data from the author’s many years of experience in teaching and consultancy
• Shows how Bayesian methods can be used to analyze survival data
• Includes details on how R can be used to carry out all the methods described, with guidance on the interpretation of the resulting output
• Contains many real data examples and additional datasets that can be used for coursework
• All datasets used are available in electronic format from the publisher’s website

Modelling Survival Data in Medical Research, Fourth Edition, is an invaluable resource for statisticians in the pharmaceutical industry and biomedical research centers, research scientists and clinicians who are analyzing their own data, and students following undergraduate or postgraduate courses in survival analysis.