The objective of this paper is to provide an introduction to the principles of Bayesian joint modeling of longitudinal measurements and time-to-event outcomes, as well as model implementation using the BUGS language syntax. This syntax can be executed directly using OpenBUGS or by utilizing convenient functions to invoke OpenBUGS and JAGS from R software. In this paper, all details of joint models are provided, ranging from simple to more advanced models. The presentation started with the joint modeling of a Gaussian longitudinal marker and time-to-event outcome. The implementation of the Bayesian paradigm of the model is reviewed. The strategies for simulating data from the JM are also discussed. A proportional hazard model with various forms of baseline hazards, along with the discussion of all possible association structures between the two sub-models are taken into consideration. The paper covers joint models with multivariate longitudinal measurements, zero-inflated longitudinal measurements, competing risks, and time-to-event with cure fraction. The models are illustrated by the analyses of several real data sets. All simulated and real data and code are available at \url{https://github.com/tbaghfalaki/JM-with-BUGS-and-JAGS}.

翻译：本文旨在介绍纵向测量与时间-事件结果的贝叶斯联合建模原理，以及使用BUGS语言语法实现模型的方法。该语法可直接通过OpenBUGS执行，或利用便捷函数从R软件调用OpenBUGS与JAGS。本文提供了从简单到高级联合模型的完整细节，首先阐述高斯纵向标记与时间-事件结果的联合建模，回顾该模型的贝叶斯范式实现方法，并讨论了从联合模型中模拟数据的策略。研究考虑了具有多种基线风险形式的比例风险模型，以及两个子模型间所有可能的关联结构。本文涵盖多变量纵向测量、零膨胀纵向测量、竞争风险及含治愈比例的时间-事件联合模型，并通过多个实际数据集的分析进行模型演示。所有模拟数据、真实数据及代码均公开于\url{https://github.com/tbaghfalaki/JM-with-BUGS-and-JAGS}。