Modelling Survival Data in Medical Research (Chapman & Hall/CRC Texts in Statistical Science) 4th Edition
by David Collett(Author)
Publisher finelybook 出版社: Chapman and Hall/CRC; 4th edition (May 31, 2023)
Language 语言: English
Print Length 页数: 540 pages
ISBN-10: 1032252855
ISBN-13: 9781032252858
Book Description
By finelybook
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 summarising survival data, Cox regression and parametric modelling, the book covers many more advanced techniques, including interval-censoring, frailty modelling, 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 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 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 publisher’s website
Modelling Survival Data in Medical Research, Fourth 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.
