Data and Statistical Sciences, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, USA.
Data and Statistical Sciences, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, USA.
Contemp Clin Trials. 2022 Apr;115:106717. doi: 10.1016/j.cct.2022.106717. Epub 2022 Feb 28.
In clinical studies, it is common to have binary outcomes collected over time as repeated measures. This manuscript reviews and evaluates two popular classes of statistical methods for analyzing binary response data with repeated measures: likelihood-based Generalized Linear Mixed Model (GLMM), and semiparametric Generalized Estimating Equation (GEE). Recommendations for choice of analysis model and points to consider for implementation in clinical studies in the presence of missing data are provided based on a comprehensive literature review, as well as, a simulation study evaluating the performance of both GLMM and GEE under scenarios representative of typical clinical trial settings. Under Missing at Random (MAR) assumption, GLMM is preferred over GEE, and the SAS PROC GLIMMIX marginal model is recommended for implementing GLMM in analyzing clinical trial data. When there is an underlying continuous variable used to define the binary response, and the missing proportion is high and/or unbalanced between treatment groups, a two-step approach combining Multiple Imputation (MI) and GEE (MI-GEE) is recommended.
在临床研究中，通常会随着时间的推移收集重复测量的二进制结果。本文综述和评估了两种分析具有重复测量的二项反应数据的常用统计方法：基于似然的广义线性混合模型（GLMM）和半参数广义估计方程（GEE）。基于全面的文献回顾和模拟研究，针对在存在缺失数据的情况下，为选择分析模型和实施临床试验提供了建议，模拟研究评估了 GLMM 和 GEE 在典型临床试验环境下的代表性场景下的性能。在随机缺失（MAR）假设下，GLMM 优于 GEE，建议在分析临床试验数据时使用 SAS PROC GLIMMIX 边际模型来实现 GLMM。当存在用于定义二项反应的潜在连续变量，且缺失比例较高且/或在处理组之间不平衡时，建议采用两步法，结合多重插补（MI）和 GEE（MI-GEE）。
