An efficient algorithm to determine the optimal two-stage randomized multinomial designs in oncology clinical trials.

Sun et al. (2009) proposed an optimal two-stage randomized multinomial design that incorporates both response rate (RR) and early progression rate (EPR) in designing phase II oncology trials. However, determination of the design parameters in their approach requires evaluating huge numbers of combinations among possible values of design parameters, and thus requires highly intensive computation. In this paper we develop an efficient algorithm to identify the optimal two-stage randomized multinomial designs in phase II oncology clinical trials comparing a treatment arm to a control arm. The proposed algorithm substantially reduces the computation intensity via an approximation method. Some other techniques are also used to further improve its efficiency. Examples show that the proposed algorithm has more than a 90% reduction in computation time while having an acceptably low approximation error. This may enhance usage of the optimal two-stage multinomial design in clinical trials and also make it feasible to extend the design to more complicated scenarios.