Defining Utility as a Measure of Preference Under Uncertainty in Phase I-II Oncology Dose Finding Trials

TL;DR

This paper introduces a Bayesian decision-theoretic framework for dose finding in oncology trials, using a new utility function based on efficacy and toxicity that incorporates clinical risk attitudes and reference points.

Contribution

It proposes R2DT, a novel utility-based method that captures clinician preferences and improves dose selection accuracy in phase I-II oncology trials.

Findings

R2DT outperforms traditional methods in identifying optimal doses.

The utility function effectively models clinician risk attitudes.

Simulation shows good operating characteristics of the proposed framework.

Abstract

The main objective of dose finding trials is to find an optimal dose amongst a candidate set for further research. The trial design in oncology proceeds in stages with a decision as to how to treat the next group of patients made at every stage until a final sample size is reached or the trial stopped early. This work applies a Bayesian decision-theoretic approach to the problem, proposing a new utility function based on both efficacy and toxicity and grounded in von Neumann-Morgenstern (VNM) utility theory. Our proposed framework seeks to better capture real clinical judgements by allowing attitudes to risk to vary when the judgements are of gains or losses, which are defined with respect to an intermediate outcome known as a reference point. We call this method Reference Dependent Decision Theoretic dose finding (R2DT). A simulation study demonstrates that the framework can perform well and produce good operating characteristics. The simulation results demonstrate that R2DT is better at detecting the optimal dose in scenarios where candidate doses are around minimum acceptable efficacy and maximum acceptable toxicity thresholds. The proposed framework shows that a flexible utility function, which better captures clinician beliefs, can lead to trials with good operating characteristics, including a high probability of finding the optimal dose. Our work demonstrates proof-of-concept for this framework, which should be evaluated in a broader range of settings.