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

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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/

Table of Contents

Introduction
Inferential Objectives in Longitudinal Studies
Case Studies
Organization of the Book

Analysis of Longitudinal Data
Features of Repeated Measures Data
Linear Mixed Effects Models
Dropout in Longitudinal Studies

Analysis of Time-to-Event Data
Features of Event Time Data
Relative Risk Models
Time-Dependent Covariates

Joint Models for Longitudinal and Time-to-Event Data
The Standard Joint Model
Connection with the Dropout Framework

Extensions of the Standard Joint Model
Parameterizations
Multiple Failure Times
Latent Class Joint Models

Diagnostics
Residuals for the Longitudinal Submodel
Residuals for the Survival Submodel
Random Effects Distribution

Prediction and Accuracy in Joint Models
Dynamic Predictions for the Survival and Longitudinal Outcomes
Effect of the Parameterization on Predictions
Prospective Accuracy Measures for Longitudinal Markers

Author Bio(s)

Editorial Reviews

"The book is well written in a matter-of-fact style that makes even unfamiliar readers understand the concept of joint models and furthermore provides them with a guide for getting started with their own analysis. The more joint model-savvy reader will, on the other hand, find inspiration for further foraging into the subject of model extensions, diagnostics, prediction, and accuracy. … a handy guide for anyone with a need to analyze survival data in the presence of a time-dependent covariate that is measured several times. As the author incorporates a longitudinal model for such a covariate into the relative risk regression modeling framework, we observe the advantage of being able to account for measurement errors within our covariate; a fortification of our research outcomes. All in all a satisfying book on joint models with a solid payout for fellow researchers."
—Maral Saadati, Biometrical Journal, 55, 2013

"This new addition to the genre is based on the JM package written by the author and has been done well. … I particularly liked the sections on numerical methods, which manage to give a useful overview of what the package is actually doing but without scaring off the mathematically reluctant. The dreaded problem of non-convergence is met head-on, with an illustration and discussion of how a little knowledge of the fitting algorithms can help to overcome such problems. This alone is worth the price of the book! … To summarize, this is a very well-crafted introduction to an active research area that I would recommend to anyone interested in getting into this field or in learning to analyze such data."
—Geoff Jones, Australian & New Zealand Journal of Statistics, 2013