Joint models (JM) for longitudinal and survival data have gained increasing interest and found applications in a wide range of clinical and biomedical settings. These models facilitate the understanding of the relationship between outcomes and enable individualized predictions. In many applications, more complex event processes arise, necessitating joint longitudinal and multistate models. However, their practical application can be hindered by computational challenges due to increased model complexity and large sample sizes. Motivated by a longitudinal multimorbidity analysis of large UK health records, we have developed a scalable Bayesian methodology for such joint multistate models that is capable of handling complex event processes and large datasets, with straightforward implementation. We propose two blockwise inference approaches for different inferential purposes based on different levels of decomposition of the multistate processes. These approaches leverage parallel computing, ease the specification of different models for different transitions, and model/variable selection can be performed within a Bayesian framework using Bayesian leave-one-out cross-validation. Using a simulation study, we show that the proposed approaches achieve satisfactory performance regarding posterior point and interval estimation, with notable gains in sampling efficiency compared to the standard estimation strategy. We illustrate our approaches using a large UK electronic health record dataset where we analysed the coevolution of routinely measured systolic blood pressure (SBP) and the progression of multimorbidity, defined as the combinations of three chronic conditions. Our analysis identified distinct association structures between SBP and different disease transitions.

翻译：摘要：纵向数据与生存数据的联合模型（JM）在临床和生物医学领域受到日益广泛的关注，并应用于多种场景。这些模型有助于理解结局变量间的关联，并能实现个体化预测。在许多应用中，更复杂的事件过程会产生，这需要构建纵向与多状态数据的联合模型。然而，由于模型复杂度增加和大样本量带来的计算难题，其实际应用常受阻碍。受大型英国健康记录中纵向多病共存分析的启发，我们开发了一种可扩展的贝叶斯方法论，用于此类多状态联合模型，该方法能够处理复杂事件过程和大规模数据集，且实现简便。我们基于多状态过程的不同分解程度，针对不同推断目的提出了两种分块推断方法。这些方法可利用并行计算，简化不同状态转移模型的指定，并能在贝叶斯框架下通过贝叶斯留一交叉验证进行模型/变量选择。通过模拟研究，我们证明所提方法在后验点估计和区间估计方面取得了令人满意的性能，且与标准估计策略相比，在采样效率上有显著提升。我们使用大型英国电子健康记录数据集验证了方法，分析了常规测量的收缩压（SBP）与多病共存进展（定义为三种慢性病的组合）的协同演化关系。我们的分析识别出SBP与不同疾病转移之间的独特关联结构。