Modelling Survival Data in Medical Research, 4th Edition: A Comprehensive Guide

Modelling Survival Data in Medical Research, Fourth Edition, is a comprehensive guide to the analysis of survival data, featuring a wide range of examples from biomedical research. This book is written in a non-technical style, focusing on practical applications of the techniques. It covers standard methods for summarising survival data, Cox regression, and parametric modelling, as well as advanced techniques such as interval-censoring, frailty modelling, competing risks, analysis of multiple events, and dependent censoring.

This new edition includes chapters on Bayesian survival analysis and the use of the 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 the analysis of interval-censored survival data.

Key Features:

• A comprehensive account of a wide range of statistical methods for analysing survival data
• Practical guidance on modelling survival data from the authors’ 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 the interpretation of the resulting output
• Contains many real data examples and additional data sets that can be used for coursework
• All data sets used are available in electronic format from the publishers’ website

A Valuable Resource for:

• Statisticians in the pharmaceutical industry and biomedical research centres
• Research scientists and clinicians who are analysing their own data
• Students following undergraduate or postgraduate courses in survival analysis

Modelling Survival Data in Medical Research, Fourth Edition, is an invaluable resource for those working in the field of survival analysis. With its comprehensive coverage of statistical methods and practical guidance, this book is a must-have for anyone looking to improve their understanding and application of survival data analysis.