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Abstract:
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Cluster randomized trials, which often enroll a small number of clusters, can benefit from the use of constrained randomization to balance potentially prognostic covariates during the design phase. In covariate-constrained randomization, a final randomization scheme is selected from a list of randomizations that are known to balance chosen cluster-level covariates between the trial arms. Previous literature has addressed the suitability of adjusting the analysis for the covariates that were balanced in the design phase when the outcome is continuous or binary. The performance of model-based and permutation tests was compared. Here we extended this work to time-to-event outcomes. We conducted a simulation study to assess type 1 error rates and power between simple randomization and constrained randomization using both prognostic and non-prognostic covariates. We analyzed the data using a semi-parametric Cox proportional hazards model with a robust variance, a Cox proportional hazards mixed effects model, and a permutation test. A current cluster randomized trial of vector control for the prevention of mosquito-borne disease in children in Mexico is used as a motivating example.
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