A phase II trial design with direct assignment option for initial marker validation.

Abstract

34 Background: Phase II clinical trials aim to identify promising experimental regimens for further testing in phase III trials. Testing targeted therapies with predictive biomarkers mandates efficient trial designs. Current biomarker-based trial designs, including the enrichment, all-comers, and adaptive designs, randomize patients to receive treatment or not throughout the entire duration of the trial. Recognizing the need for randomization yet acknowledging the possibility of promising but nonconclusive results after a preplanned interim analysis (IA), we propose a two-stage phase II design that allows for the possibility of direct assignment (i.e., stop randomization and assign all patients to the experimental arm in stage II) based on IA results. Methods: Using simulations, we compared properties of the direct assignment option design to a 1:1 randomized phase II design and assessed the impact of the timing of IA (after 33%, 50%, or 67% of accrual) and number of IA (one versus two with option for direct assignment at the first and second) over a range of response rate ratios (between 1.0 and 3.0). Results: Between 12% and 30% of the trials (out of 6,000 simulated trials) adopt direct assignment in stage II, with direct adoption depending on the treatment effect size and specified type I error rate (TIER). The direct assignment option design has minimal loss in power (<1.8%) and minimal increase in T1ER (<2.1%) compared to a 1:1 randomized design. The maximum loss in power across possible timings of IA was <1.2%. For the direct assignment option design, there was a 20%-50% increase in the number of patients treated on the experimental (vs. control) arm for the 1 IA case, and 40%-100% increase for the 2 IA case. Conclusions: Testing predictive biomarkers in clinical trials requires new design strategies. In the spectrum of phase II designs from adaptive to balanced randomized all-comers or enrichment designs, the direct assignment design provides a middle ground with desirable statistical properties that may appeal to both clinicians and patients.