In most models and algorithms for dose-finding clinical trials, it is assumed that the trial participants are homogeneous-the optimal dose is the same for all those who qualify for the trial. However, if there are heterogeneous populations who may benefit from the same treatment, it is inefficient to conduct dose-finding separately for each group, and assuming homogeneity across all subpopulations may lead to identification of the incorrect dose for some (or all) subgroups. To accommodate heterogeneity in dose-finding trials when both efficacy and toxicity outcomes must be used to identify the optimal dose (as in immunotherapeutic oncology treatments), we utilize an adaptive Bayesian clustering method which borrows strength among similar subgroups and clusters truly homogeneous subgroups. Unlike methodology already described in the literature, our proposed methodology does not require the assumption of exchangeability between subgroups or a priori ordering of subgroups, but does allow for specification of different subgroup-specific priors if prior information is available. We provide a comparison of operating characteristics between our method and Bayesian hierarchical models for subgroups in a variety of relevant scenarios. After simulation studies with four a priori subgroups, we observed that our method and the hierarchical models both outperform separate subgroup-specific models when all subgroups have the same dose-efficacy and dose-toxicity curves. However, our method outperforms hierarchical models when one subgroup has a different dose-efficacy or dose-toxicity curve from the other three subgroups.
Keywords: Bayesian model averaging; dose-finding clinical trial; spike-and-slab prior; subgroup.
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.