混合效应包络模型
Mixed Effects Envelope Models
摘要：
当随着时间的推移重复收集多个度量时，响应之间通常存在冗余。包络方法是近年来提出的一种在不损失信息的情况下对多变量响应进行降维的方法。它比标准的最小二乘估计器具有更高的效率。本文将包络方法推广到具有非平衡设计和时变预测变量的纵向数据混合效应模型。我们表明，我们的模型提供了更有效的估计比混合效应模型中的标准估计。在模拟和控制糖尿病心血管风险行动（ACCORD）研究中观察到所提出的方法比标准混合效应模型估计的准确性和效率提高。
英文摘要：
When multiple measures are collected repeatedly over time, redundancy typically exists among responses. The envelope method was recently proposed to reduce the dimension of responses without loss of information in regression with multivariate responses. It can gain substantial efficiency over the standard least squares estimator. In this paper, we generalize the envelope method to mixed effects models for longitudinal data with possibly unbalanced design and time-varying predictors. We show that our model provides more efficient estimators than the standard estimators in mixed effects models. Improved accuracy and efficiency of the proposed method over the standard mixed effects model estimator are observed in both the simulations and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
