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局的贝叶斯联合建模原理，以及利用BUGS语言语法实现模型的流程。该语法可直接通过OpenBUGS执行，也可利用便捷函数从R软件调用OpenBUGS和JAGS。本文提供了从简单到高级的联合模型全部细节，首先从高斯型纵向标记物与时间事件结局的联合建模展开，回顾了该模型贝叶斯范式的实现方法，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，并探讨了两个子模型之间所有可能的关联结构。本文涵盖了多变量纵向测量、零膨胀纵向测量、竞争风险以及含治愈率的时间事件结局等联合模型，并通过多个真实数据集的分析加以示例说明。所有模拟与真实数据及代码均可在\url{https://github.com/tbaghfalaki/JM-with-BUGS-and-JAGS}获取。