Keywords: PoC trial, Bayesian, Dual-criterion
Proof-of-concept (PoC) trials play a critical role in the clinical development of an experimental therapy. Typically, these trials offer the first read-out of efficacy and lead to one of the following decisions: consider further development (GO), stop development (NO-GO), or seek further information. To achieve that goal, statistical significance is assessed but usually fails to produce efficient decision in absence of clinically-relevant effect estimates. To palliate this, we propose a dual-criterion design which formally combines a statistical and clinical criterion. The dual-criterion design requires two inputs; a null hypothesis (i.e., no effect) and a decision value (i.e., minimum effect estimate). Unlike the standard design, with statistical significance as the sole success criterion, the decision value is explicit while the study power is implicit. Sample size determination in dual-criterion design requires special attention regarding operating characteristics (i.e., error rates) and implied study outcomes. We successfully applied the dual-criterion design in oncology Phase II trials with binary and time-to-event endpoints. The evaluation covered a characterization of the decision criteria, sample size, as well as data scenarios and operating characteristics. Yet, despite their apparent simplicity, those designs can be conceptually challenging especially in terms of implementation and communication between the statistician and the trial design team. When properly understood and well executed, dual-criterion design based on statistical significance and clinically relevant effect size improves evidence-based, quantitative decision-making in early phase PoC trials.