Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Department of Statistics, Florida State University, Tallahassee, FL 32306, United States.
Biometrics. 2024 Jan 29;80(1). doi: 10.1093/biomtc/ujae003.
The aim of this paper is to systematically investigate merging and ensembling methods for spatially varying coefficient mixed effects models (SVCMEM) in order to carry out integrative learning of neuroimaging data obtained from multiple biomedical studies. The "merged" approach involves training a single learning model using a comprehensive dataset that encompasses information from all the studies. Conversely, the "ensemble" approach involves creating a weighted average of distinct learning models, each developed from an individual study. We systematically investigate the prediction accuracy of the merged and ensemble learners under the presence of different degrees of interstudy heterogeneity. Additionally, we establish asymptotic guidelines for making strategic decisions about when to employ either of these models in different scenarios, along with deriving optimal weights for the ensemble learner. To validate our theoretical results, we perform extensive simulation studies. The proposed methodology is also applied to 3 large-scale neuroimaging studies.
本文旨在系统地研究空间变系数混合效应模型（SVCMEM）的合并和集成方法，以便对来自多个生物医学研究的神经影像学数据进行综合学习。“合并”方法涉及使用包含所有研究信息的综合数据集来训练单个学习模型。相反，“集成”方法涉及创建来自各个研究的不同学习模型的加权平均值。我们系统地研究了在不同程度的研究间异质性存在下，合并和集成学习者的预测准确性。此外，我们还为在不同情况下何时使用这些模型中的任意一个提供了策略性决策的渐近指南，并为集成学习者推导出了最优权重。为了验证我们的理论结果，我们进行了广泛的模拟研究。所提出的方法也应用于 3 个大规模的神经影像学研究。
