Improving the performance of Bayesian logistic regression model with overdose control in oncology dose-finding studies.

Autor: Zhang H; Merck & Co., Inc., West Point, Pennsylvania, USA., Chiang AY; Bristol Myers Squibb, Berkeley Heights, New Jersey, USA., Wang J; Global Biometrics and Data Sciences, Bristol Myers Squibb, Boudry, Switzerland.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2022 Nov 30; Vol. 41 (27), pp. 5463-5483. Date of Electronic Publication: 2022 Apr 15.
DOI: 10.1002/sim.9402
Abstrakt: 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 article, 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 the original BLRM. Simulation results reveal that the new designs have better accuracy and treat more patients at MTD.
(© 2022 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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