使用面向纵向数据和事件发生时间数据的贝叶斯联合模型高效分析大型患者登记库
Efficiently analyzing large patient registries with Bayesian joint models for longitudinal and time-to-event data
AI总结：
针对贝叶斯联合模型处理大型患者登记库计算慢的问题，提出数据拆分并行采样+精度加权平均合并的方法，在囊性纤维化登记库数据上实现85%计算时间缩减，已集成至JMbayes2包。
摘要：
纵向结果与事件发生时间结果的联合建模已成为随访研究中的常用工具。然而，将贝叶斯联合模型拟合到患者登记库这类大型数据集时，可能需要极长的计算时间。为加快采样速度，我们将一份患者登记库数据集划分为多个子样本，并行分析这些子样本，再将得到的马尔可夫链蒙特卡洛采样结果合并为共识分布。我们通过模拟研究探究了不同共识策略在联合模型中的表现，具体对比了将所有采样结果直接合并、等权重平均以及精度加权平均三种方式，研究场景覆盖了不同样本量、数据拆分数量和处理器配置。采样过程的并行化大幅缩短了模型运行时间。我们发现，大样本量下的加权平均共识分布与目标后验分布几乎完全一致。该算法已被整合到联合模型的R包JMbayes2中公开可用。本研究的动因源于临床研究需求：即利用美国囊性纤维化基金会患者登记库（包含35153名个体，累计随访时长372366年）的数据，探究常用肺功能指标ppFEV1与肺移植或死亡风险之间的关联。将该登记库拆分为5个子样本后，计算时间从9.22小时降至1.39小时，减少了85%。研究表明，拆分数据并通过精度加权平均得到共识分布，是联合建模框架下处理大型数据集的一种计算高效且稳健的方法。
英文摘要：
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.
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