Seamless Phase I-II Cancer Clinical Trials Using Kernel-Based Covariate Similarity.

In response to the U.S. Food and Drug Administration's (FDA) Project Optimus, a paradigm shift is underway in the design of early-phase oncology trials. To accelerate drug development, seamless Phase I/II designs have gained increasing attention, along with growing interest in the efficient reuse of Phase I data. We propose a nonparametric information-borrowing method that adaptively discounts Phase I observations according to the similarity of covariate distributions between Phase I and Phase II. Similarity is quantified using a kernel-based maximum mean discrepancy (MMD) and transformed into a dose-specific weight incorporated into a power-prior framework for Phase II efficacy evaluation, such as for the objective response rate (ORR). Considering the small sample sizes typical of early-phase oncology studies, we analytically derive a confidence interval for the weight, enabling assessment of borrowing precision without resampling procedures. Simulation studies under four toxicity scenarios and five baseline-covariate settings showed that the proposed method improved the probability that the lower bound of the 95% credible interval for ORR exceeded a prespecified threshold at efficacious doses, while avoiding false threshold crossings at weakly efficacious doses. A case study based on a metastatic pancreatic ductal adenocarcinoma trial illustrates the resulting borrowing weights and posterior estimates.