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

Longitudinal studies often investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest. An example is prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. This book provides a full treatment of joint models for longitudinal and time-to-event data. The content is explanatory rather than mathematically rigorous and emphasizes applications. All illustrations put forward are available in the R programming language via the freely available package JM written by the author.

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.