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Data Analysis with Competing Risks and Intermediate States

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
Data Analysis with Competing Risks and Intermediate States provides a comprehensive guide to models and techniques for analysing competing risks and intermediate states, focusing on recent estimation methods and detailed interpretation of results.

Beginning with biomedical and epidemiological examples, the book defines key concepts and explores nonparametric estimation. It introduces the use of stacked data sets to model covariable effects on transition rates between states and discusses three approaches to quantifying effects on a cumulative scale.

Each chapter includes exercises, software guidance in SAS, Stata, and R, and practical computer tasks using a bone marrow transplant data set, supported by R code available on the book’s website.
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Format: Paperback / softback
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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?

This book is ideal for researchers with some experience in time-to-event analysis seeking to advance their skills in competing risks and multi-state models. It is also accessible to professionals from other fields needing to apply these methods in their own domains.

Book Hero thinking about your next read

This practical and thorough book explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results. Each chapter includes standard exercises; a software section on SAS, Stata, and

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

Data Analysis with Competing Risks and Intermediate States explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results.

After introducing example studies from the biomedical and epidemiological fields, the book formally defines the concepts that play a role in analyses with competing risks and intermediate states. It addresses nonparametric estimation of the relevant quantities. The book then shows how to use a stacked data set that offers great flexibility in the modelling of covariable effects on the transition rates between states. It also describes three ways to quantify effects on the cumulative scale.

Each chapter includes standard exercises that reflect on the concepts presented, a section on software that explains options in SAS and Stata and the functionality in the R programme, and computer practicals that allow readers to practice with the techniques using an existing data set of bone marrow transplant patients. The book’s website provides the R code for the computer practicals along with other material.

For researchers with some experience in the analysis of standard time-to-event data, this practical and thorough treatment extends their knowledge and skills to the competing risks and multi-state settings. Researchers from other fields can also easily translate individuals and diseases to units and phenomena from their own areas.

Book Details

INFORMATION

ISBN: 9780367738051

Publisher: Taylor & Francis Ltd

Format: Paperback / softback

Date Published: 18 December 2020

Country: United Kingdom

Imprint: Chapman & Hall/CRC

Audience: General / adult, Tertiary education

DIMENSIONS

Width: 156.0mm

Height: 234.0mm

Weight: 520g

Pages: 277

About the Author

Ronald B. Geskus is an associate professor at the Academic Medical Center in Amsterdam. He received a Ph.D. in mathematics from the Delft Technical University. His main research interests include competing risks and multi-state models, prediction of events based on time-updated marker values, and causal inference.

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