![]() A necessary (but not sufficient) condition for the estimate of the treatment effect to be unbiased is that subjects in a given treatment group are not contaminated by those in another. Randomized controlled trials are considered the gold standard for evaluating the effect of a new treatment relative to an old treatment, a placebo or no treatment at all. All relevant covariates should be carefully identified, be actually measured and included in the statistical model to avoid severe levels of parameter and standard error bias and insufficient power levels. Otherwise more sophisticated methods to randomize clusters to treatments should be used, such as stratification or balance algorithms. The possibility of covariate imbalance should be taken into account while calculating the sample size of a cluster randomized trial. The consequences are most severe for large clusters and/or small intraclass correlation coefficients since then the required number of clusters to achieve a desired power level is smallest. Power levels based on the unadjusted linear mixed model are often too low. The results are more severe for the unadjusted linear mixed model: parameter biases up to 100 % and standard error biases up to 200 % may be observed. Ignoring the possibility of covariate imbalance while calculating the sample size at the cluster level may result in a loss in power of at most 25 % in the adjusted linear mixed model. The results show covariate imbalance results in negligible parameter bias and small standard error bias in adjusted linear mixed models. The covariate is binary and measured at the cluster level the outcome is continuous and measured at the individual level. The factors in this study are the degree of imbalance, the covariate effect size, the cluster size and the intraclass correlation coefficient. ![]() The consequences of covariance imbalance in unadjusted and adjusted linear mixed models are investigated by means of a simulation study. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. ![]() The number of clusters in a cluster randomized trial is often low. ![]()
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