Randomization in clinical trials with small sample sizes using group sequential designs

Background: Group sequential designs, which allow early stopping for efficacy or futility, may benefit from balanced sample sizes at interim and final analyses. This requirement for balance limits the choice of admissible randomization procedures. We investigate if the choice of randomization procedure, whether balanced or not, impacts the type I error probability and power in small sample clinical trials with group sequential designs.Methods: We start with a literature review to assess how randomization procedures are reported in group sequential trials. We then investigate the impact of randomization procedures on the type I error probability and power of trials with Pocock, O’Brien-Fleming, Lan-DeMets and inverse normal combination test designs.Results: Our findings show that only a limited number of published group sequential trials report sufficient randomization details. Simulation results demonstrate that deficiencies in the implementation of randomization can inflate type I error rates. Some combinations of group sequential designs and randomization procedures cause a loss of power, for example, when using inverse normal combination tests. When the planned balanced allocation ratio in (interim) analyses cannot be ensured, the Lan-DeMets approach is preferable for small sample trials due to its robustness to deviations between the planned and observed allocation ratio. The inverse normal combination test, while useful in trials with limited prior information, should be used cautiously with permuted block randomization that maintains the planned allocation ratio to avoid power loss.Conclusion: We propose a framework for selecting the most suitable combinations of group sequential design and randomization procedure for small sample clinical trials. Our findings highlight the need for improved reporting of randomization methods in group sequential clinical trials. Further, the validity of some of the group sequential designs relies on the application of appropriate randomization procedures which were difficult to identify in the literature and is possibly violated.