Improving the Performance of Bayesian Logistic Regression Model with Overdose Control in Oncology Dose-Finding Studies

TL;DR

This paper enhances Bayesian logistic regression models for oncology dose-finding by balancing overdose and underdose controls, leading to improved accuracy and patient treatment at the maximum tolerated dose.

Contribution

It introduces a novel dose-finding design that addresses the conservativeness of existing BLRM by incorporating underdose control, improving performance in identifying MTD.

Findings

Better accuracy in MTD identification

Treats more patients at MTD

Reduces conservativeness of original BLRM

Abstract

An accurately identified maximum tolerated dose (MTD) serves as the cornerstone of successful subsequent phases in oncology drug development. Bayesian logistic regression model (BLRM) is a popular and versatile model-based dose-finding design. However, BLRM with original overdose control strategy has been reported to be safe but "excessively conservative". In this manuscript, we investigate the reason for conservativeness and point out that a major reason could be the lack of appropriate underdose control. We propose designs that balance overdose and underdose control to improve the performance over original BLRM. Simulation results reveal that the new designs have better accuracy and treat more patients at MTD.