Optimal adaptive single-arm phase II trials under quantified uncertainty

Single-arm trials with binary endpoint are firmly established in e.g., early clinical oncology. Here, two-stage designs are often employed to allow early termination of the trial in the case of an unexpectedly large or small response rate to the new treatment. Various designs have been proposed over the last few years which usually require strong assumptions about the true response rate during planning. Often, these designs are not robust to deviations from the planning assumptions. In this paper, we define a Bayesian framework for scoring two-stage designs under uncertainty and investigate the characteristics of designs optimizing a commonly employed performance score of Liu et al. The resulting optimal designs are compared with an alternative, utility-based approach incorporating expected power and sample size. We provide insights in the underlying implicit assumptions of using expected power for scoring adaptive designs and relate the global score function to the practice of sample size recalculation based on conditional power. An in-depth comparison of the features of the different performance scores and their respective optimizing designs provides the guidance for practitioners who face the problem of choosing between the various options. A software implementation of the proposed methods is publicly available online.