Comb-BOIN12: A Utility-Based Bayesian Optimal Interval Design for Dose Optimization in Cancer Drug-Combination Trials

Abstract

Drug combinations are increasingly utilized in cancer treatment to enhance drug effectiveness through synergistic therapeutic effects. However, determining the optimal biological dose combination (OBDC) in small-scale drug combination trials presents challenges due to the increased complexity of the dose space. To effectively optimize the dose combination of combined drugs, we propose a model-assisted design by extending the single-agent Bayesian optimal interval phase I/II (BOIN12) design. Our approach incorporates a utility function to balance the trade-off between risk and benefit and directly models the utility of each dose by constructing a quasi-beta-binomial model. A key advantage of our design is the simplification of decision-making during interim periods by considering all possible outcomes and pre-including the decision rule in the protocol. Additionally, we present a time-to-event (TITE) version of our design, employing an approximate likelihood approach to mitigate potential late-onset effects. We demonstrate that our proposed design exhibits robust and desirable operating characteristics across various scenarios through extensive simulation studies.

KEY WORDS:

• Dose optimization
• Drug combination
• Risk-benefit trade-off
• Bayesian adaptive design
• Model-assisted design

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Funding

The author(s) reported there is no funding associated with the work featured in this article.