发表机构
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本教程介绍使用R包JMbayes2拟合纵向与时间-事件数据的扩展联合模型，涵盖竞争风险、复发事件等复杂场景，并提供分步指南和模拟数据示例。
AI 中文摘要
共享参数的纵向与时间-事件数据联合模型是分析重复测量的生物标志物、临床事件及其之间复杂关系的强大工具。近期的方法学进展已将最初为单一事件时间和连续纵向生物标志物开发的基本框架扩展到更复杂的场景。本教程提供了使用R包JMbayes2拟合纵向与时间-事件数据扩展联合模型的分步指南。我们涵盖了一系列应用，包括具有竞争风险、复发事件、多状态过程、灵活关联结构以及遵循不同分布的多个纵向结果的模型。每个模型均使用与真实世界数据集高度相似的模拟数据进行说明，并详细解释了数据组织、模型规格、拟合、诊断和解释。本教程面向有兴趣使用可访问且可复现的R代码分析自身数据的应用研究人员。
英文摘要
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.
