The joint modeling of longitudinal and time-to-event outcomes has become a popular tool in follow-up studies. However, fitting Bayesian joint models to large datasets, such as patient registries, can require extended computing times. To speed up sampling, we divided a patient registry dataset into subsamples, analyzed them in parallel, and combined the resulting Markov chain Monte Carlo draws into a consensus distribution. We used a simulation study to investigate how different consensus strategies perform with joint models. In particular, we compared grouping all draws together with using equal- and precision-weighted averages. We considered scenarios reflecting different sample sizes, numbers of data splits, and processor characteristics. Parallelization of the sampling process substantially decreased the time required to run the model. We found that the weighted-average consensus distributions for large sample sizes were nearly identical to the target posterior distribution. The proposed algorithm has been made available in an R package for joint models, JMbayes2. This work was motivated by the clinical interest in investigating the association between ppFEV1, a commonly measured marker of lung function, and the risk of lung transplant or death, using data from the US Cystic Fibrosis Foundation Patient Registry (35,153 individuals with 372,366 years of cumulative follow-up). Splitting the registry into five subsamples resulted in an 85\% decrease in computing time, from 9.22 to 1.39 hours. Splitting the data and finding a consensus distribution by precision-weighted averaging proved to be a computationally efficient and robust approach to handling large datasets under the joint modeling framework.

翻译：纵向与时间事件结局的联合建模已成为随访研究中的常用工具。然而，将贝叶斯联合模型应用于大型数据集（如患者登记数据）时，可能需要较长的计算时间。为加速采样，我们将患者登记数据集划分为子样本，并行分析这些子样本，并将生成的马尔可夫链蒙特卡罗抽样结果整合为共识分布。我们通过模拟研究探讨了不同共识策略在联合模型中的表现，特别比较了将所有抽样结果合并与使用等权平均及精度加权平均方法。研究考虑了不同样本量、数据分割数量及处理器特性的场景。并行化抽样过程显著缩短了模型运行时间。我们发现，对于大样本量，加权平均共识分布与目标后验分布几乎一致。该算法已在联合模型的R包JMbayes2中实现。本研究源于临床需求——利用美国囊性纤维化基金会患者登记数据（包含35,153例个体，累计随访372,366人年），探究常用肺功能指标ppFEV1与肺移植或死亡风险之间的关联。将登记数据分为五个子样本后，计算时间从9.22小时降至1.39小时，降幅达85%。数据分割与精度加权平均共识分布方法被证明是在联合建模框架下处理大型数据集时兼具计算效率与鲁棒性的手段。