Joint Models for Longitudinal and Time-to-Event Data: With Applications in R

The book "Joint Models for Longitudinal and Time-to-Event Data: With Applications in R" provides a comprehensive treatment of random effects joint models for longitudinal and time-to-event outcomes, with applications in R. It focuses on explanatory applications and includes mathematical details for understanding key features. All illustrations are implemented in the R programming language using the author's freely available package JM.

Format: Paperback / softback
Length: 278 pages
Publication date: 21 January 2023
Publisher: Taylor & Francis Ltd

In longitudinal studies, it becomes crucial to explore the relationship between a marker that is repeatedly measured over time and the timing of an event of interest, such as the occurrence of prostate cancer. When longitudinal PSA level measurements are collected alongside the time-to-recurrence in prostate cancer studies, for instance, it becomes essential to understand how these markers are associated with the event of interest.

To address this research question, Joint Models for Longitudinal and Time-to-Event Data: With Applications in R offers a comprehensive treatment of random effects joint models for longitudinal and time-to-event outcomes. These models provide a powerful tool for analyzing such data, allowing researchers to account for the correlation between repeated measurements and the occurrence of the event of interest.

The content of the book is primarily explanatory, focusing on the applications of joint modeling in longitudinal and time-to-event data analysis. However, sufficient mathematical details are provided to ensure that readers with a background in statistics can grasp the key features of these models. Throughout the book, illustrative examples are presented to demonstrate the practical implications of joint modeling. These examples can be implemented in the R programming language using the freely available package JM, which was developed by the author.

To further enhance the accessibility of the book, all the R code used in it is available at: http://jmr.r-forge.r-project.org/. This resource allows readers to follow along with the examples and gain a deeper understanding of the concepts and techniques discussed.

In conclusion, Joint Models for Longitudinal and Time-to-Event Data: With Applications in R is a valuable resource for researchers and practitioners interested in analyzing longitudinal and time-to-event data. By providing a comprehensive treatment of random effects joint models, the book enables researchers to explore the associations between repeated measurements and the occurrence of events of interest, leading to insights into various fields such as health sciences, epidemiology, and social sciences.

Weight: 430g
Dimension: 233 x 155 x 21 (mm)
ISBN-13: 9781032477565