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/
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