Joint Models for Longitudinal and Time-to-Event Data With Applications in R [Hardcover]

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 Rprovides 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.

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
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