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

Music City Center 

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 and power, and to achieve small sample size and other favorable operating characteristics. This paper proposes a general method to reduce the number of parameters using group stepwise methods and Monte Carlo simulations, significantly decreasing the number of iterations required to identify near-optimal parameters. The key idea is the use of the Hwang-Shih-DeCani family of error-spending functions, which use just two parameters (an alpha-spending parameter γ_α and a beta-spending parameter γ_β) that determine sensible stopping boundaries for efficacy and futility, respectively. The algorithm then optimally determines stopping boundaries in such a way that power is maximized and overall Type I error is strictly controlled at a predetermined α level. Our method extends classical group sequential designs, but does not rely on normality assumptions, and can accommodate complex trial designs. We illustrate in the case of a multi-arm clinical trial with one control arm and k treatment arms, and where interim analyses are performed when 30%, 50%, and 70% of the total sample size have been accumulated. Our approach delivers boundaries that offer significant average sample size reductions under both the null and alternative hypotheses.

adaptive trials

Optimal clinical trial design