Selection of Step －in Dosing Regimen based on Bayesian Model in Early Clinical Trials
10.11783/j.issn.1002－3674.2025.02.002
- VernacularTitle:早期临床试验中基于贝叶斯模型的递进给药方案选择
- Author:
Zihan ZHU
1
;
Zihang ZHONG
1
;
Senmiao NI
1
Author Information
1. 南京医科大学公共卫生学院生物统计学系(211166)
- Publication Type:Journal Article
- Keywords:
Clinical trial;
Step－in dosing regimen;
Bayesian model
- From:
Chinese Journal of Health Statistics
2025;42(2):166-170,174
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore a Bayesian logistic regression model for step－in dosing regimens(eBLRM),which considers the cumulative toxicity probability across different dosing cycles to identify the maximum tolerated schedule(MTS).Methods The Bayesian logistic regression model(BLRM)was extended to obtain a posterior estimate for the cumulative toxicity probability of the last cycle based on accumulated patient data,enabling exploration of dose sequences.Results The performance of eBLRM was evaluated by comparison with the existing methods.Simulation results indicated that eBLRM performed better or equivalent in the proportion of the correct selection of MTS and patients assigned to real MTS under low－toxicity scenarios.In the case of high－toxicity scenarios,eBLRM had a higher proportion of early trial termination due to safety,resulting in slightly inferior performance compared to the existing method.Conclusion The eBLRM method demonstrates relatively good performance,providing a simple and comprehensible dose exploration approach for step－in dosing regimens.
