A Bayesian Hybrid Design with Borrowing from Historical Study

TL;DR

This paper introduces a Bayesian hybrid design for early phase drug combination trials that leverages historical data to improve decision-making and study efficiency, with a flexible and computationally efficient framework.

Contribution

It develops a Bayesian dynamic power prior framework for controlled information borrowing in hybrid trial designs, enhancing flexibility and interpretability.

Findings

Improved power in detecting drug activity compared to traditional designs

Closed-form posterior distribution for computational efficiency

Validated through simulations and a case study

Abstract

In early phase drug development of combination therapy, the primary objective is to preliminarily assess whether there is additive activity from a novel agent when combined with an established monotherapy. Due to potential feasibility issues for conducting a large randomized study, uncontrolled single-arm trials have been the mainstream approach in cancer clinical trials. However, such trials often present significant challenges in deciding whether to proceed to the next phase of development due to the lack of randomization in traditional two-arm trials. A hybrid design, leveraging data from a completed historical clinical study of the monotherapy, offers a valuable option to enhance study efficiency and improve informed decision-making. Compared to traditional single-arm designs, the hybrid design may significantly enhance power by borrowing external information, enabling a more robust assessment of activity. The primary challenge of hybrid design lies in handling information borrowing. We introduce a Bayesian dynamic power prior (DPP) framework with three components of controlling amount of dynamic borrowing. The framework offers flexible study design options with explicit interpretation of borrowing, allowing customization according to specific needs. Furthermore, the posterior distribution in the proposed framework has a closed form, offering significant advantages in computational efficiency. The proposed framework's utility is demonstrated through simulations and a case study.