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

Modelling Survival Data in Medical Research, 4th Edition, is a comprehensive guide to the analysis of survival data, featuring a wide range of examples from biomedical research. The book is written in a non-technical style, focusing on practical applications of the techniques discussed. 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 R software. Earlier chapters have been extensively revised and expanded to incorporate new material on several topics, including 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.

The book provides an accessible account of a wide range of statistical methods for analysing survival data, offering practical guidance on modelling survival data from the authors’ many years of experience in teaching and consultancy. It also shows how Bayesian methods can be used to analyse survival data and includes details on how R can be used to carry out all the methods described, with guidance on the interpretation of the resulting output.

Modelling Survival Data in Medical Research, 4th Edition, is an invaluable resource for statisticians in the pharmaceutical industry and biomedical research centres, research scientists and clinicians who are analysing their own data, and students following undergraduate or postgraduate courses in survival analysis. All data sets used are available in electronic format from the publishers’ website.

Key Features:

• A comprehensive guide to the analysis of survival data, featuring a wide range of examples from biomedical research.
• 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.
• Includes chapters on Bayesian survival analysis and the use of R software.
• Provides 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 and includes details on how R can be used to carry out all the methods described, with guidance on the interpretation of the resulting output.
• Features 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.

Product Details:

• Publisher: Chapman and Hall/CRC
• Edition: 4th edition (May 31, 2023)
• Language: English
• Format: Hardcover
• Pages: 540
• ISBN-10: 1032252855
• ISBN-13: 978-1032252858