Integrating high-dimensional, heterogeneous data from multi-site cohort studies with complex hierarchical structures poses significant feature selection and prediction challenges. We extend the Bayesian Integrative Analysis and Prediction (BIP) framework to enable simultaneous feature selection and outcome modeling in data of nested hierarchical structure. We apply the proposed Bayesian Integrative Mixed Modeling (BIPmixed) framework to the Adolescent Brain Cognitive Development (ABCD) Study, leveraging multi-view data, including structural and functional MRI and early life adversity (ELA) metrics, to identify relevant features and predict the behavioral outcome. BIPmixed incorporates 2-level nested random effects, to enhance interpretability and make predictions in hierarchical data settings. Simulation studies illustrate BIPmixed's robustness in distinct random effect settings, highlighting its use for complex study designs. Our findings suggest that BIPmixed effectively integrates multi-view data while accounting for nested sampling, making it a valuable tool for analyzing large-scale studies with hierarchical data.

翻译：整合来自具有复杂层次结构的多中心队列研究的高维异构数据，带来了显著的特征选择与预测挑战。我们扩展了贝叶斯整合分析与预测框架，使其能够在嵌套层次结构的数据中同时进行特征选择和结局建模。我们将所提出的贝叶斯整合混合建模框架应用于青少年大脑与认知发展研究，利用多视图数据（包括结构和功能磁共振成像以及早期生活逆境指标）来识别相关特征并预测行为结局。BIPmixed 框架纳入了两级嵌套随机效应，以增强可解释性并在层次数据设置中进行预测。模拟研究展示了 BIPmixed 在不同随机效应设置下的稳健性，突显了其在复杂研究设计中的应用价值。我们的研究结果表明，BIPmixed 在考虑嵌套抽样的同时，能有效整合多视图数据，使其成为分析具有层次结构的大规模研究的有价值工具。