Joint Models for Longitudinal and Time-to-Event Data : With Applications in R
Language: English
Published by Taylor & Francis Ltd (Sales), 2023
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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nach der Bestellung gedruckt Neuware - Printed after ordering - In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author.All the R code used in the book is available at:jmr.r-forge.r-project.org/.
Seller Inventory # 9781032477565
- Title
- Joint Models for Longitudinal and Time-to-Event Data : With Applications in R
- Author
- Dimitris Rizopoulos
- Publisher
- Taylor & Francis Ltd (Sales)
- Publication year
- 2023
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1032477563
- ISBN 13
- 9781032477565
- Item weight
- 444 grams
- Dimensions
- 233x154x18 mm
In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models.
All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author.
All the R code used in the book is available at:
http://jmr.r-forge.r-project.org/
"Synopsis" may belong to another edition of this title.
About the Author
Dimitris Rizopoulos is an Assistant Professor at the Department of Biostatistics of the Erasmus University Medical Center in the Netherlands. Dr. Rizopoulos received his M.Sc. in Statistics in 2003 from the Athens University of Economics and Business, and a Ph.D. in Biostatistics in 2008 from the Katholieke Universiteit Leuven.
Dr. Rizopoulos wrote his dissertation, as well as a number of methodological articles on various aspects of joint models for longitudinal and time-to-event data. He currently serves as an Associate Editor for Biometrics and Biostatistics, and has been a guest editor for a special issue in joint modeling techniques in Statistical Methods in Medical Research.
"About the title" may belong to another edition of this title.
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