An optimal two-stage exploratory basket trial design with aggregated futility analysis

A basket trial investigates the effects of one drug on multiple tumor indications. To discontinue potentially inactive indications early, an interim futility analysis is usually conducted for each indication individually once it reaches the pre-specified sample size. As enrollment rates vary among indications, the futility decisions for slowenrolling indications could be made much later than other fast-enrolling indications, which could delay the overall decision for the trial significantly. To accelerate the futility decision in early-stage exploratory basket trials and potentially reallocate resources to other compounds earlier while still controlling the global type-I and type-II errors, we propose an optimal two-stage basket trial design with one aggregated futility analysis by aggregating (e.g., pooling) all indications together. The total sample size across all indications is pre-specified for the futility analysis, while the sample size per indication can be adapted to the enrollment rate. The final analysis is performed using the pruning and pooling approach (Chen et al. 2016). The design parameters are optimized by minimizing the expected total sample size under the null hypothesis, while explicitly controlling the global type-I and the type-II error rates. Simulation studies demonstrate that the proposed design has better operating characteristics than the designs with individual futility analysis (Zhou et al. 2019; Wu et al. 2021), while allowing for earlier futility decision.

Automated summary

Basket trials investigate the effects of one drug on multiple tumor indications. An optimal two-stage basket trial design is proposed for earlier futility decision and resource reallocation, with better operating characteristics compared to designs with individual futility analysis.