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

Dimitris Rizopoulos

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June 22, 2012 by Chapman and Hall/CRC
Professional - 275 Pages - 36 B/W Illustrations
ISBN 9781439872864 - CAT# K13371
Series: Chapman & Hall/CRC Biostatistics Series

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Features

  • Provides a complete treatment of joint models for longitudinal and time-to-event data
  • Introduces various extensions of the standard joint model, including several parameterizations for the association structure and the handling of competing risks
  • Covers several diagnostic tools based on residuals to assess the assumptions behind a joint model
  • Discusses dynamic predictions for the survival and longitudinal outcomes, and discrimination concepts for longitudinal markers
  • Emphasizes applications so readers understand the type of research questions best answered with joint models

Summary

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/