Leyrat Clémence, Caille Agnès, Foucher Yohann, Giraudeau Bruno
INSERM U1153, Paris, France.
INSERM CIC 1415, Tours, France.
BMC Med Res Methodol. 2016 Jan 22;16:9. doi: 10.1186/s12874-015-0100-4.
Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required.
We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs.
The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40% of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection.
The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.
尽管进行了随机分组，但整群随机试验（CRT）中仍可能出现基线不平衡和混杂偏倚。如果协变量不平衡出现在预后因素上，可能会危及统计推断的有效性。因此，诊断这种不平衡对于在需要时调整统计分析至关重要。
我们开发了一种基于倾向得分（PS）模型的c统计量的工具，以检测CRT中的全局基线协变量不平衡，并评估混杂偏倚的风险。我们进行了一项模拟研究，以评估所提出工具的性能，并应用该方法分析了来自2项已发表的CRT的数据。
对于大样本量（每组n = 500）以及与基线协变量总数相比不平衡协变量数量不太小时（≥40%的不平衡协变量），所提出的方法具有良好的性能。我们还提供了一种策略，用于预先选择PS模型中需要纳入的协变量，以增强不平衡检测。
所提出的工具在决定是否需要在对CRT进行统计分析之前进行协变量调整方面可能会有所帮助。
