We introduce a statistical framework for combining data from multiple large longitudinal cardiovascular cohorts to enable the study of long-term cardiovascular health starting in early adulthood. Using data from seven cohorts belonging to the Lifetime Risk Pooling Project (LRPP), we present a Bayesian hierarchical multivariate approach that jointly models multiple longitudinal risk factors over time and across cohorts. Because few cohorts in our project cover the entire adult lifespan, our strategy uses information from all risk factors to increase precision for each risk factor trajectory and borrows information across cohorts to fill in unobserved risk factors. We develop novel diagnostic testing and model validation methods to ensure that our model robustly captures and maintains critical relationships over time and across risk factors.

翻译：我们提出一种统计框架，用于整合多个大型纵向心血管队列数据，从而支持从成年早期开始研究长期心血管健康状况。基于寿命风险汇集项目（LRPP）中七个队列的数据，我们提出一种贝叶斯分层多元方法，能够同时对跨队列、随时间变化的多种纵向风险因素进行联合建模。由于项目中覆盖完整成年期的队列较少，我们的策略利用所有风险因素的信息来提高各风险因素轨迹的估计精度，并通过跨队列信息借用以填补未观测风险因素的数据空缺。我们开发了新颖的诊断检验与模型验证方法，以确保模型能够稳健地捕捉并保持风险因素间随时间变化的关键关联关系。