MODELLING SURVIVAL DATA IN MEDICAL RESEARCH – DAVID COLLETT

Modelling Survival Data in Medical Research, Fourth Edition, describes the analysis of survival data, illustrated with 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 summarising survival data, Cox regression and parametric modelling, the book covers many advanced techniques, including interval‑censoring, frailty modelling, competing risks, analysis of multiple events, and dependent censoring.

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

The book is an invaluable resource for statisticians in the pharmaceutical industry and biomedical research centres, research scientists and clinicians who analyse their own data, and students in undergraduate or postgraduate courses in survival analysis.

Key features:
– Presents an accessible account of a wide range of statistical methods for analysing survival data
– Contains practical guidance on modelling survival data from the author’s many years of experience in teaching and consultancy
– Shows how Bayesian methods can be used to analyse survival data
– Includes details on how R can be used to carry out all the methods described, with guidance on interpreting the resulting output
– Contains many real data examples and additional data sets that can be used for coursework