Joint models for longitudinal and time-to-event data are widely used in many disciplines. Nonetheless, existing model comparison criteria do not indicate whether a model adequately fits the data or which components may be misspecified. We introduce a Bayesian posterior predictive checks framework for assessing a joint model's fit to the longitudinal and survival processes and their association. The framework supports multiple settings, including existing subjects, new subjects with only covariates, dynamic prediction at intermediate follow-up times, and cross-validated assessment. For the longitudinal component, goodness-of-fit is assessed through the mean, variance, and correlation structure, while the survival component is evaluated using empirical cumulative distributions and probability integral transforms. The association between processes is examined using time-dependent concordance statistics. We apply these checks to the Bio-SHiFT heart failure study, and a simulation study demonstrates that they can identify model misspecification that standard information criteria fail to detect. The proposed methodology is implemented in the freely available R package JMbayes2.

翻译：纵向数据与时间-事件数据的联合模型已在众多学科领域得到广泛应用。然而，现有的模型比较准则无法有效判断模型是否充分拟合数据，亦难以识别具体哪些模型成分可能存在误设。本文提出一种贝叶斯后验预测检验框架，用于评估联合模型对纵向过程、生存过程及其关联结构的拟合优度。该框架支持多种应用场景，包括对已有受试者的分析、仅含协变量的新受试者预测、随访中间时点的动态预测以及交叉验证评估。对于纵向成分，拟合优度通过均值、方差及相关结构进行检验；生存成分则采用经验累积分布函数与概率积分变换进行评估。两过程间的关联性通过时依一致性统计量进行检验。我们将该方法应用于Bio-SHiFT心力衰竭研究，并通过模拟研究证明：相较于标准信息准则，本方法能够有效识别模型误设情形。所提出的方法已在开源R包JMbayes2中实现。