Department of Global Statistical Sciences, Eli Lilly and Company, Indianapolis, Indiana.
Pharm Stat. 2020 Jul;19(4):482-491. doi: 10.1002/pst.2006. Epub 2020 Feb 27.
Generalized linear mixed models (GLMM) are commonly used to model the treatment effect over time while controlling for important clinical covariates. Standard software procedures often provide estimates of the outcome based on the mean of the covariates; however, these estimates will be biased for the true group means in the GLMM. Implementing GLMM in the frequentist framework can lead to issues of convergence. A simulation study demonstrating the use of fully Bayesian GLMM for providing unbiased estimates of group means is shown. These models are very straightforward to implement and can be used for a broad variety of outcomes (eg, binary, categorical, and count data) that arise in clinical trials. We demonstrate the proposed method on a data set from a clinical trial in diabetes.
广义线性混合模型（GLMM）常用于在控制重要临床协变量的情况下随时间建模治疗效果。标准软件程序通常根据协变量的平均值提供结果的估计值；然而，对于 GLMM 中的真实组均值，这些估计值将存在偏差。在频率主义框架中实施 GLMM 可能会导致收敛问题。本文展示了使用完全贝叶斯 GLMM 提供组均值无偏估计的模拟研究。这些模型非常简单，可以用于临床试验中出现的广泛的各种结果（例如，二项式、分类和计数数据）。我们在来自糖尿病临床试验的数据集上演示了所提出的方法。
