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

arXiv: https://arxiv.org/abs/2105.06855 · HTML: https://mlsanity.com/paper/2105.06855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2021.

## Built on by (most cited first, 2 of 2)

- [AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunities](https://mlsanity.com/paper/2311.18725/) (2023)
- [Shared Keyboard: An improved Bayesian design for phase I clinical trials via Beta kernel process](https://mlsanity.com/paper/2605.25043/) (2026)

## Abstract (arXiv)

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.