Oncology Dose Finding Using Approximate Bayesian Computation Design

TL;DR

This paper introduces a novel approximate Bayesian computation (ABC) method for phase I cancer trial design that is model-free, efficient, and robust, improving dose finding without relying on dose-toxicity models.

Contribution

The paper presents a new ABC-based phase I trial design that avoids model assumptions and efficiently utilizes all accumulated data for dose assignment.

Findings

Demonstrates robustness and efficiency through extensive simulations.

Shows improved dose selection accuracy over traditional methods.

Successfully applied to a clinical trial with promising results.

Abstract

In the development of new cancer treatment, an essential step is to determine the maximum tolerated dose (MTD) via phase I clinical trials. Generally speaking, phase I trial designs can be classified as either model-based or algorithm-based approaches. Model-based phase I designs are typically more efficient by using all observed data, while there is a potential risk of model misspecification that may lead to unreliable dose assignment and incorrect MTD identification. In contrast, most of the algorithm-based designs are less efficient in using cumulative information, because they tend to focus on the observed data in the neighborhood of the current dose level for dose movement. To use the data more efficiently yet without any model assumption, we propose a novel approximate Bayesian computation (ABC) approach for phase I trial design. Not only is the ABC design free of any dose--toxicity curve assumption, but it can also aggregate all the available information accrued in the trial for dose assignment. Extensive simulation studies demonstrate its robustness and efficiency compared with other phase I designs. We apply the ABC design to the MEK inhibitor selumetinib trial to demonstrate its satisfactory performance. The proposed design can be a useful addition to the family of phase I clinical trial designs due to its simplicity, efficiency and robustness.