Modelling Survival Data in Medical Research
4th Edition
Published on 31. May 2023
556 pages
978-1-000-86310-9 (ISBN)
Description
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.
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.
More details
Series
Edition
4th edition
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Illustrations
131 Line drawings, black and white; 131 Illustrations, black and white
ISBN-13
978-1-000-86310-9 (9781000863109)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
David Collett
Modelling Survival Data in Medical Research
Book
05/2023
4th Edition
Chapman & Hall/CRC
€120.30
Shipment within 10-20 days
Person
David Collett obtained his first degree at the University of Leicester, before going on to complete an MSc in statistics at the University of Newcastle and a PhD in statistics at the University of Hull. David was a lecturer and senior lecturer in the Department of Applied Statistics at the University of Reading for over 25 years, including eight years as head of that department. In 2003, he was appointed Associate Director of Statistics and Clinical Studies at NHS Blood and Transplant. This involved supervising the statistical work of over 30 staff and collaborative work with transplant clinicians and research scientists. David became Director of the NHS Blood and Transplant Clinical Trials Unit, responsible for the design, conduct and analysis of clinical trials in transplantation and transfusion medicine. He also held a visiting chair in the Southampton Statistical Sciences Research Institute, University of Southampton, until his retirement. He now likes to spend as much time as possible on the golf course.
Content
1. Survival analysis 2. Some non-parametric procedures 3. The Cox regression model 4. Model checking in the Cox regression model 5. Parametric regression models 6. Flexible parametric models 7. Model checking in parametric models 8. Time-dependent variables 9. Interval-censored survival data 10. Frailty models 11. Non-proportional hazards and institutional comparisons 12 Competing risks 13. Multiple events and event history modelling 14. Dependent censoring 15. Sample size requirements for a survival study 16. Bayesian survival analysis 17. Survival Analysis with R
