Kalaylioglu Zeynep, Demirhan Haydar
1 Department of Statistics, Middle East Technical University, Ankara, Türkiye.
2 Department of Statistics, Hacettepe University, Beytepe, Ankara, Türkiye.
Stat Methods Med Res. 2017 Dec;26(6):2885-2896. doi: 10.1177/0962280215615003. Epub 2015 Nov 6.
Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors.
联合混合模型是一种用于分析在主要终点测量的标量响应以及协变量纵向测量值的有吸引力的方法。在这些模型的标准贝叶斯分析中，测量误差方差和随机效应的方差/协方差是先验独立建模的。关键在于，鉴于响应中的总变化，不能假定这些方差是独立的。本文提出了一种联合贝叶斯分析，其中这些方差项是先验联合建模的。模拟表明，与使用独立方差先验的分析相比，使用多元方差先验进行分析通常会导致后验推断中的偏差减小（相对偏差更小）和效率提高（四分位间距更小）。
