Application of the Bayesian mixture model based on a principal stra-tum strategy in clinical trials
10.12092/j.issn.1009-2501.2025.07.009
- VernacularTitle:基于主层策略的贝叶斯混合模型在临床试验中的应用
- Author:
Yiwen WU
1
;
Yue SUN
;
Zixuan LU
;
Jiahe PAN
;
Er YU
;
Hongmei WO
;
Shaowen TANG
;
Yang ZHAO
;
Juncheng DAI
;
Honggang YI
Author Information
1. 南京医科大学公共卫生学院生物统计学系,南京 211166,江苏
- Publication Type:Journal Article
- Keywords:
Bayesian statistics;
mixture models;
principal stratum strategy;
non-inferiority trials;
noncompliance
- From:
Chinese Journal of Clinical Pharmacology and Therapeutics
2025;30(7):942-949
- CountryChina
- Language:Chinese
-
Abstract:
AIM:To evaluate the application effec-tiveness of a Bayesian mixture model based on the principal stratum strategy for estimating the com-plier average causal effect(CACE)in clinical trials with non-compliance.METHODS:Using a non-infe-riority randomized controlled trial investigating a novel drug for primary type 2 diabetes mellitus(non-inferiority margin:-0.4)as a case study,the primary analysis applied a Bayesian mixture model under the monotonicity assumption to estimate CACE of between-group differences in glycated he-moglobin(HbA1c)changes within the compliant stratum,followed by non-inferiority testing.Sensi-tivity analyses included a Bayesian mixture model relaxing the monotonicity assumption and compar-ing results with per-protocol set(PPS)analysis.RE-SULTS:In the primary analysis,the posterior mean of CACE for HbA1c change in the compliant stratum was 0.081％,with a one-sided 97.5％credible inter-val lower bound of-0.124,exceeding the non-infe-riority margin(-0.4％),supporting the non-inferiori-ty efficacy of the novel drug in the compliant stra-tum(P(H1|Data)=1).Consistent findings were ob-served in PPS analyses(estimated effect:0.136％;one-sided 97.5％credible interval lower bound:-0.069％),further validating methodological robust-ness.CONCLUSION:In clinical trials with noncom-pliance as an intercurrent event,the Bayesian mix-ture model under the principal stratum strategy ef-fectively adjusts for compliance-related bias and yields conservative,robust estimates of causal ef-fects,supporting its value in efficacy evaluation un-der complex compliance scenarios.
