Keywords: biomarker stratified design, multi-arm trials, shared control, FWER, log-rank statistic
It is a common scenario that an experimental oncology therapy, as a monotherapy, may be more effective than standard of care (SOC) in a biomarker positive population but less so or even inferior to SOC in biomarker negative population. At the same time, due to synergistic or additive effect, the combination of the two may be more effective than SOC alone in the all-comer population. The conventional development paradigm is to conduct two separate Phase III trials, one with the monotherapy versus SOC in the biomarker positive population, and the other with the combination therapy versus SOC in the all-comer population. In this presentation, we propose a one-trial design that stratifies by biomarker status and randomizes biomarker positive patients into three arms (combination therapy, monotherapy, and SOC) and biomarker negative patients into two arms (combination therapy and SOC). There are two hypotheses in the proposed design and each addresses a different question. The family-wise type-I error rate (FWER) is smaller, due to shared control, than that of two separate trials. Therefore, no FWER adjustment is necessary in the proposed design and each hypothesis can be tested at the conventional 2.5% (one-sided) alpha level. The population for comparison between the combination therapy and SOC is skewed in the proposed design. A two-step log-rank statistic is proposed to account for the skewness. Power and sample size of the proposed design are evaluated in comparison with the two-trial paradigm. The proposed design is more efficient.