Li Baoyue, Bruyneel Luk, Lesaffre Emmanuel
Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands.
Stat Med. 2014 May 20;33(11):1877-99. doi: 10.1002/sim.6062. Epub 2013 Dec 9.
A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach.
传统的高斯分层模型假设均值具有嵌套的多级结构，且每个层级的方差恒定。我们提出了一种贝叶斯多元多级因子模型，该模型假设均值和协方差矩阵都具有多级结构。也就是说，除了均值的多级结构外，我们还假设协方差矩阵依赖于协变量和随机效应。这使得我们能够探究协方差结构是否依赖于更高层级的值，从而对跨更高层级值的多元结果的方差和相关结构中的异质性进行建模。该方法应用于一项大型欧洲研究中收集的护士职业倦怠测量的三维向量，以回答研究问题：结果的协方差矩阵是否不仅依赖于护理组织中记录的系统层面特征，还依赖于随国家、医院和护理单元而变化的未记录因素。模拟结果展示了我们建模方法的性能。
