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

Authors

Kana Makino, Natsumi Makigusa, Masahiro Kojima

Published in

Pharmaceutical statistics. Volume 25. Issue 5. Pages e70107.

Abstract

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.

PMID:
42629327
Bibliographic data and abstract were imported from PubMed on 22 Aug 2026.

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