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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.
Covariate Measurement Errors and Parameter-Estimation in a Failure Time Regression-Model.
Joint Models for Longitudinal and Time-to-Event Data with Applications in R by Dimitris Rizopoulos. Chapter 1 Chapter 2 Chapter 3 Chapter 4 Section 4.2 Section 4.3.5 Section 4.3.7 Section 4.4.1 Section 4.4.2 Section 4.5 Section 4.7 Chapter 5 ...
Joint Models for Longitudinal and Time‐to‐Event Data with Applications in Rizopoulos, R.
However, much of this research has concentrated on a single longitudinal outcome and a single event time outcome. In clinical and public health research, patients who are followed up over time may often experience multiple ...