Optimal curtailed designs for single arm phase II clinical trials
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In single-arm phase II oncology trials, the most popular choice of design is Simon's two-stage design, which allows early stopping at one interim analysis. However, the expected trial sample size can be reduced further by allowing curtailment. Curtailment is stopping when the final go or no-go decision is certain, so-called non-stochastic curtailment, or very likely, known as stochastic curtailment. In the context of single-arm phase II oncology trials, stochastic curtailment has previously been restricted to stopping in the second stage and/or stopping for a no-go decision only. We introduce two designs that incorporate stochastic curtailment and allow stopping after every observation, for either a go or no-go decision. We obtain optimal stopping boundaries by searching over a range of potential conditional powers, beyond which the trial will stop for a go or no-go decision. This search is novel: firstly, the search is undertaken over a range of values unique to each possible design realisation. Secondly, these values are evaluated taking into account the possibility of early stopping. Finally, each design realisation's operating characteristics are obtained exactly. The proposed designs are compared to existing designs in a real data example. They are also compared under three scenarios, both with respect to four single optimality criteria and using a loss function. The proposed designs are superior in almost all cases. Optimising for the expected sample size under either the null or alternative hypothesis, the saving compared to the popular Simon's design ranges from 22
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