Modelling Survival Data in Medical Research (Texts in Statistical Science)

David Collett

ISBN 10: 1439856788 / ISBN 13: 9781439856789

Published by Chapman and Hall/CRC, 2014

About this Item

Hardcover, third edition, xvi + 532 pages, shipping weight over 1kg, NOT ex-library. Dusty handling marks on the outer page edges. Interior is crisp, clean and bright throughout with unmarked text, free of inscriptions and stamps, firmly bound. Issued without a dust jacket. -- This cornerstone text in the Chapman & Hall/CRC Texts in Statistical Science series provides a highly practical, non-mathematical introduction to the analysis of "time-to-event" data, specifically tailored for clinicians and medical statisticians. Core Focus and Content: The book is renowned for its clarity, focusing on the application of statistical models rather than dense mathematical proofs. - Survival Functions: Detailed guidance on estimating survival using the Kaplan-Meier and Nelson-Aalen methods. - Proportional Hazards: Extensive coverage of the Cox Regression Model, including checking the proportional hazards assumption and handling time-dependent variables. - Parametric Models: Comparison of Weibull, exponential, and log-logistic models for survival data. - Advanced Topics: Introduction to interval-censored data, frailty models (for correlated data), and competing risks. Key Features of the 3rd Edition: - Software Integration: Includes updated examples using R and SAS, reflecting modern computational standards in medical research. - New Content: Features expanded sections on sample size calculation and the analysis of recurrent event data. - Case Studies: Uses real-world medical datasets (e.g., remission times for leukemia patients or survival after heart transplants) to illustrate diagnostic techniques and model selection. - Model Diagnostics: Strong emphasis on residual analysis and identifying influential observations to ensure model validity. -- David Collett is a former Associate Director of Statistics and Clinical Audit at NHS Blood and Transplant and was previously a Professor of Medical Statistics at the University of Reading. His background in both academia and practical healthcare application makes this text a staple for "bench-to-bedside" research.

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Bibliographic Details

• Title: Modelling Survival Data in Medical Research (Texts in Statistical Science)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2014
• Language: English
• ISBN 10: 1439856788
• ISBN 13: 9781439856789
• Binding: Hardcover
• Edition: 3rd Edition
• Condition: Very Good

About this title

Synopsis

Modelling Survival Data in Medical Research describes the modelling approach to the analysis of survival data using a wide range of examples from biomedical research.

Well known for its nontechnical style, this third edition contains new chapters on frailty models and their applications, competing risks, non-proportional hazards, and dependent censoring. It also describes techniques for modelling the occurrence of multiple events and event history analysis. Earlier chapters are now expanded to include new material on a number of topics, including measures of predictive ability and flexible parametric models. Many new data sets and examples are included to illustrate how these techniques are used in modelling survival data.

Bibliographic notes and suggestions for further reading are provided at the end of each chapter. Additional data sets to obtain a fuller appreciation of the methodology, or to be used as student exercises, are provided in the appendix. All data sets used in this book are also available in electronic format online.

This book is an invaluable resource for statisticians in the pharmaceutical industry, professionals in medical research institutes, scientists and clinicians who are analyzing their own data, and students taking undergraduate or postgraduate courses in survival analysis.

About the Author

David Collett, PhD, associate director of statistics and clinical studies, NHS Blood and Transplant, Bristol and visiting professor of statistics, Southampton Statistical Sciences Research Institute, University of Southampton, UK