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. Our modeling reveals substantial age-related variation in risk factor trajectories, with patterns that differ across life stages, subgroups, and cohorts, thereby highlighting key periods for cardiovascular prevention and monitoring. Keywords: Bayesian hierarchical models; Missing data; Model validation; Multiple imputation; Random effects.

翻译：我们提出了一种统计框架，用于整合多个大型纵向心血管队列的数据，从而能够从成年早期开始研究长期心血管健康。利用来自终身风险汇集项目（LRPP）中七个队列的数据，我们采用贝叶斯分层多变量方法，该方法同时建模多个随时间变化的纵向风险因素，并跨队列进行整合。由于项目中很少有队列覆盖整个成年期，我们的策略利用所有风险因素的信息来提高每个风险因素轨迹的精确度，并跨队列借用信息来填补未观测到的风险因素。我们开发了新的诊断测试和模型验证方法，以确保我们的模型能够稳健地捕获并维持随时间及跨风险因素的关键关系。我们的建模揭示了风险因素轨迹中显著的年龄相关变异，其模式在不同生命阶段、亚组和队列之间存在差异，从而突出了心血管预防和监测的关键时期。关键词：贝叶斯分层模型；缺失数据；模型验证；多重插补；随机效应。