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Applied Survival Analysis

Regression Modeling of Time-to-Event Data
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Book Hero Magic crafted this summary to help describe this book. While it's new and still learning, it may not be perfect - your feedback is welcome! Summary
Applied Survival Analysis, Second Edition is a comprehensive guide to regression modelling for time-to-event data, focusing on practical applications in medical, epidemiological, and biostatistical research. It emphasises contemporary modelling techniques with clear explanations, real-world examples, and case studies. Key topics include variable selection, interactions, model diagnostics, recurrent event and frailty models, competing risks, and handling missing data, all demonstrated using Stata®.
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Format: Hardback
$31899
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Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

Ideal for graduate students and researchers in biostatistics, epidemiology, medical statistics, and health-related fields seeking a practical approach to survival data analysis.

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This book fills this gap, providing a comprehensive, self-contained introduction to regression modeling used in the analysis of time-to-event data in epidemiological, biostatistical, and other health-related research.

Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! Description
The most practical, up-to-date guide to modelling and analyzing time-to-event data—now in a valuable new edition.

Since the publication of the first edition nearly a decade ago, analyses using time-to-event methods have increased considerably in all areas of scientific inquiry, mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research.

This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data.

Features of the Second Edition include:

  • Expanded coverage of interactions and the covariate-adjusted survival functions
  • The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques
  • New discussion of variable selection with multivariable fractional polynomials
  • Further exploration of time-varying covariates, complex with examples
  • Additional treatment of the exponential, Weibull, and log-logistic parametric regression models
  • Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values
  • New examples and exercises at the end of each chapter

Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.

Series: Wiley Series in Probability and Statistics

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Book Hero Magic summarised reviews for this book. While it's new and still learning, it may not be perfect - your feedback is welcome! HOW HAS THIS BEEN REVIEWED?

This book is praised as a practical and up-to-date resource ideal for graduate-level courses and researchers analysing survival data. Reviewers highlight its clear teaching style, detailed coverage of model fitting, and the availability of online data sets, making it an excellent applied survival analysis text.

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Book Details

INFORMATION

ISBN: 9780471754992

Publisher: John Wiley & Sons Inc

Format: Hardback

Date Published: 15 April 2008

Country: United States

Imprint: Wiley-Interscience

Edition: 2nd edition

Illustration: Charts: 2 B&W, 0 Color; Screen captures: 100 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 0 B&W, 0 Color

Audience: Professional and scholarly

DIMENSIONS

Spine width: 28.0mm

Width: 155.0mm

Height: 236.0mm

Weight: 748g

Pages: 416

About the Author

David W. Hosmer, PhD, is Professor Emeritus of Biostatistics in the School of Public Health and Heatlth Sciences at the University of Massachusetts Amherst. Dr. Hosmer is the coauthor of Applied Logistic Regression, published by Wiley.

Stanley Lemeshow, PhD, is Professor and Dean of the College of Public Health at The Ohio State University. Dr. Lemeshow has over thirty-five years of academic experience in the areas of regression, categorical data methods, and sampling methods. He is the coauthor of Sampling of Population: Methods and Application and Applied Logistic Regression, both published by Wiley.

Susanne May, PhD, is Assistant Professor of Biostatistics at the University of California, San Diego. Dr. May has over twelve years of experience in providing statistical support for health-related research projects.

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