发表机构
Vanderbilt University Medical Center; University of California, Riverside（范德堡大学医学中心; 加州大学河滨分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对心理测量中高维多模态预测变量与缺失多变量结果，提出SHIM贝叶斯框架，结合层次马蹄收缩与缺失结果处理，模拟验证平衡灵敏度与假阳性，并应用于阿尔茨海默病队列。R包shim已公开。
AI 中文摘要
在心理测量研究中，高维、多模态预测变量以及部分观测的多变量结果十分常见。然而，现有的正则化方法通常不能适应层次化的预测变量结构，且主要针对单变量结果设计。我们提出SHIM，一种用于结构化变量选择的贝叶斯框架，它将层次化马蹄收缩与对缺失结果的贝叶斯处理相结合。该框架联合适应预测变量的层次结构、结果之间的依赖性以及不完整的多变量响应。我们建立了所提出的先验设定的理论性质，并通过模拟研究评估SHIM。结果表明，SHIM在灵敏度与假阳性控制之间取得了平衡，同时产生准确的系数估计和校准良好的不确定性量化。我们进一步将SHIM应用于一个阿尔茨海默病队列的数据，以刻画多模态神经影像测量与多变量神经心理学结果之间的关联，并为下游分析流体生物标志物与认知之间关系生成基于后验的多次插补。一个R包shim已公开提供，以促进实施。
英文摘要
High-dimensional, multimodal predictors and partially observed multivariate outcomes are common in psychometric research. However, existing regularization methods often do not accommodate hierarchical predictor structures and are primarily designed for univariate outcomes. We propose SHIM, a Bayesian framework for structured variable selection that combines hierarchical horseshoe shrinkage with a Bayesian treatment of missing outcomes. The framework jointly accommodates predictor hierarchies, dependence among outcomes, and incomplete multivariate responses. We establish theoretical properties of the proposed prior specification and evaluate SHIM through simulation studies. The results demonstrate that SHIM balances sensitivity with false-positive control while yielding accurate coefficient estimates and well-calibrated uncertainty quantification. We further apply SHIM to data from an Alzheimer's disease cohort to characterize associations between multimodal neuroimaging measures and multivariate neuropsychological outcomes and to generate posterior-based multiple imputations for downstream analyses of the relationships between fluid biomarkers and cognition. An R package, shim, is publicly available to facilitate implementation.
