Speaker
Description
Drug development in the era of precision medicine increasingly uses basket trials and other multi-subgroup designs, where targeted therapies are evaluated across biomarker-defined patient subtrials. For many targeted agents and immunotherapies, the objective in early development is no longer the maximum tolerated dose (MTD), but the optimal biological dose (OBD) that achieves the best benefit–risk trade-off between efficacy and toxicity. Identifying an OBD separately within each subtrial is challenging because sample sizes in early-phase trials are small, and toxicity and efficacy need to be considered jointly. At the same time, complete pooling (a one-size-fits-all strategy) across subtrials can yield biased recommendations since patient heterogeneity is ignored. To alleviate these concerns, we are developing a Bayesian framework for subtrial-specific OBD selection that enables robust borrowing of information on two-dimensional parameters representing toxicity and efficacy. Building on the bivariate exchangeable–non-exchangeable (E-BiEXNEX) modelling framework (arXiv:2505.10317), we specify priors on subtrial-specific dose–toxicity and dose–efficacy parameters so that information sharing is data-adaptive and resistant to negative transfer under extreme observations. Choices of prior distributions are calibrated across multiple candidates to promote robust performance over a wide range of plausible scenarios. Our primary setting assumes binary toxicity and continuous efficacy, but the approach can be extended to other endpoint types with minor modifications to the regression components. The dose recommendation is driven by a utility-based decision rule that combines posterior toxicity probabilities and efficacy means under pre-specified safety and futility constraints, yielding subtrial-specific OBD recommendations with quantified uncertainty. Operating characteristics will be evaluated under scenarios with varying degrees of between-subtrial similarity, dose–response shapes, and locations of OBD.
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