Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic

• Main Authors: Clémence Leyrat, Agnès Caille, Yohann Foucher, Bruno Giraudeau
• Format: Article
• Language: English
• Published: BMC 2016-01-01
• Series: BMC Medical Research Methodology
• Subjects: Cluster randomized trial; Confounding bias; Propensity score; C-statistic; Baseline imbalance
• Online Access: http://link.springer.com/article/10.1186/s12874-015-0100-4

Abstract Background 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. Methods 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. Results 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. Conclusion The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.