Abstract
BackgroundDespite 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.MethodsWe 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.ResultsThe 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.ConclusionThe proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-015-0100-4) contains supplementary material, which is available to authorized users.
Highlights
Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs)
It is important to note that our method focuses only on individual-level characteristics; in CRTs, clusters are the unit of randomization and any observed imbalance in cluster-level covariates will be due to sampling fluctuations
Proportion π of simulated datasets in which the estimated c-statistic was greater or equal to the threshold value defined as the 95th percentile of the c-statistic distribution in absence of systematic baseline imbalance, i.e., the proportion of situations in which baseline imbalance was detected, according to our proposed rule
Summary
Baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). In cluster randomized trials (CRTs), the units of randomization are not individuals but rather the social units to which the individuals belong [1]. This may challenge the balance between groups in terms of baseline covariates. Clusters are sometimes randomized before the identification and recruitment of participants, which may jeopardize allocation concealment [2,3,4,5]. The risk of chance imbalances increases when the number of randomized clusters decreases, which is frequent [8, 9]
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