  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.
