Pseudo cluster randomization dealt with selection bias and contamination in clinical trials

Abstract

Background and objectives: When contamination is present, randomization on a patient level leads to dilution of the treatment effect. The usual solution is to randomize on a cluster level, but at the cost of efficiency and more importantly, this may introduce selection bias. Furthermore, it may slow down recruitment in the clusters that are randomized to the "less interesting" treatment. We discuss an alternative randomization procedure to approach these problems.

Methods: Pseudo cluster randomization is a two-stage randomization procedure that balances between individual randomization and cluster randomization. For common scenarios, the design factors needed to calculate the appropriate sample size are tabulated.

Results: A pseudo cluster randomized design can reduce selection bias and contamination, while maintaining good efficiency and possibly improving enrollment. To make a well-informed choice of randomization procedure, we discuss the advantages of each method and provide a decision flow chart.

Conclusion: When contamination is thought to be substantial in an individually randomized setting and a cluster randomized design would suffer from selection bias and/or slow recruitment, pseudo cluster randomization can be considered.