A comparison of methods to analyse continuous data from pseudo cluster randomized trials

A major methodological reason to use cluster randomization is to avoid the contamination that would arise in an individually randomized design. However, when patient recruitment cannot be completed before randomization of clusters, the non-blindedness of recruiters and patients may cause selection bias, while in the control clusters, it may slow recruitment due to patient or recruiter preferences for the intervention. As a compromise, pseudo cluster randomization has been proposed. Because no insight is available into the relative performance of methods to analyse data obtained from this design, we compared the type I and II error rates of mixed models, generalized estimating equations (GEE) and a paired t-test to those of the estimator originally proposed in this design. The bias in the point estimate and its standard error were also incorporated into this comparison. Furthermore, we evaluated the effect of the weighting scheme and the accuracy of the sample size formula that have been described previously. Power levels of the originally proposed estimator and the unweighted mixed models were in agreement with the sample size formula, but the power of paired t-test fell short. GEE produced too large type I errors, unless the number of clusters was large (>30-40 per arm). The use of the weighting scheme generally enhanced the power, but at the cost of increasing the type I error in mixed models and GEE. We recommend unweighted mixed models as the best compromise between feasibility and power to analyse data from a pseudo cluster randomized trial.