Buhule O D, Wahed A S, Youk A O
Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Stat Med. 2017 Dec 20;36(29):4677-4691. doi: 10.1002/sim.7444. Epub 2017 Aug 22.
Modeling of correlated biomarkers jointly has been shown to improve the efficiency of parameter estimates, leading to better clinical decisions. In this paper, we employ a joint modeling approach to a unique diabetes dataset, where blood glucose (continuous) and urine glucose (ordinal) measures of disease severity for diabetes are known to be correlated. The postulated joint model assumes that the outcomes are from distributions that are in the exponential family and hence modeled as multivariate generalized linear mixed effects model associated through correlated and/or shared random effects. The Markov chain Monte Carlo Bayesian approach is used to approximate posterior distribution and draw inference on the parameters. This proposed methodology provides a flexible framework to account for the hierarchical structure of the highly unbalanced data as well as the association between the 2 outcomes. The results indicate improved efficiency of parameter estimates when blood glucose and urine glucose are modeled jointly. Moreover, the simulation studies show that estimates obtained from the joint model are consistently less biased and more efficient than those in the separate models.
联合建模相关生物标志物已被证明可提高参数估计的效率，从而做出更好的临床决策。在本文中，我们对一个独特的糖尿病数据集采用联合建模方法，已知糖尿病疾病严重程度的血糖（连续型）和尿糖（有序型）测量值是相关的。假定的联合模型假设结果来自指数族分布，因此被建模为通过相关和/或共享随机效应关联的多元广义线性混合效应模型。使用马尔可夫链蒙特卡罗贝叶斯方法来近似后验分布并对参数进行推断。所提出的方法提供了一个灵活的框架，以考虑高度不平衡数据的层次结构以及两个结果之间的关联。结果表明，联合对血糖和尿糖进行建模时，参数估计的效率有所提高。此外，模拟研究表明，从联合模型获得的估计值比单独模型中的估计值始终偏差更小且效率更高。
