Problem
Research questions and friction points this paper is trying to address.
joint models
longitudinal data
time-to-event data
competing risks
recurrent events
Extended Joint Models for Longitudinal and Time-to-Event Data: A Tutorial
🤖 AI Summary
本文通过使用R包JMbayes2，介绍了解决纵向和时间到事件数据联合建模问题的方法，包括处理复杂场景如竞争风险、重复事件等。
📝 Abstract
Shared-parameter joint models for longitudinal and time-to-event data are powerful tools for analyzing repeatedly measured biomarkers, clinical events, and the complex relationships between them. Recent methodological advances have extended the basic framework, which was originally developed for a single event time and a continuous longitudinal biomarker, to more complex scenarios. This tutorial provides a step-by-step guide to fitting extended joint models for longitudinal and time-to-event data using the R package JMbayes2. We cover a range of applications, including models with competing risks, recurrent events, multistate processes, flexible association structures, and multiple longitudinal outcomes following different distributions. Each model is illustrated using simulated data that closely resemble a real-world dataset, with detailed explanations of data organization, model specification, fitting, diagnostics, and interpretation. The tutorial is designed for applied researchers who are interested in analyzing their own data with extended joint models using accessible and reproducible R code.
Innovation
Methods, ideas, or system contributions that make the work stand out.
extended joint models
shared-parameter
competing risks
recurrent events
multistate processes
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