Leuven Biostatistics and Statistical Bioinformatics Centre, KU Leuven, Leuven, Belgium.
Department of Statistics, KEMRI-Wellcome Trust Research Programme - CGRMC, Kilifi, Kenya.
Stat Med. 2023 Aug 15;42(18):3128-3144. doi: 10.1002/sim.9768. Epub 2023 Jun 23.
Li et al developed a multilevel covariance regression (MCR) model as an extension of the covariance regression model of Hoff and Niu. This model assumes a hierarchical structure for the mean and the covariance matrix. Here, we propose the combined multilevel factor analysis and covariance regression model in a Bayesian framework, simultaneously modeling the MCR model and a multilevel factor analysis (MFA) model. The proposed model replaces the responses in the MCR part with the factor scores coming from an MFA model. Via a simulation study and the analysis of real data, we show that the proposed model is quite efficient when the responses of the MCR model are not measured directly but are latent variables such as the patient experience measurements in our motivating dataset.
李等人开发了一种多层次协方差回归（MCR）模型，作为 Hoff 和 Niu 的协方差回归模型的扩展。该模型假设均值和协方差矩阵具有层次结构。在这里，我们在贝叶斯框架中提出了组合的多层次因子分析和协方差回归模型，同时对 MCR 模型和多层次因子分析（MFA）模型进行建模。所提出的模型用来自 MFA 模型的因子得分替换 MCR 部分中的响应。通过模拟研究和实际数据分析，我们表明，当 MCR 模型的响应不是直接测量的而是潜在变量（如我们激励数据集中的患者体验测量）时，所提出的模型非常有效。
