Group Sequential Trial Design Using Stepwise Monte Carlo for Increased Flexibility and Robustness

Clinical trials are becoming increasingly complex, incorporating numerous parameters and degrees of freedom. Optimal analytic approaches for these intricate trial designs are often unavailable, necessitating extensive simulation to control the Type I error rate and power, while reducing sample size and achieving favorable operating characteristics. This paper proposes a general method to reduce the dimension of the design space using group stepwise methods and Monte Carlo simulations, significantly decreasing the number of iterations required to identify near-optimal parameters. The method extends classical Group Sequential Designs but does not rely on normality assumptions and can accommodate complex trial designs. We offer a simulation study comparing the optimality, precision, and efficiency (runtime) of our method to those of existing approaches and conclude that our new method offers an attractive trade-off among these three key summaries.