A basket trial design based on constrained hierarchical Bayesian model for latent subgroups

I类和II类错误 贝叶斯概率 样本量测定 计算机科学 贝叶斯定理 计量经济学 统计 数学 人工智能
作者
Kentaro Takeda,Atsuki Hashimoto,Shufang Liu,Alan Rong
出处
期刊:Journal of Biopharmaceutical Statistics [Taylor & Francis]
卷期号:: 1-12
标识
DOI:10.1080/10543406.2024.2311851
摘要

It is well known a basket trial consisting of multiple cancer types has the potential of borrowing strength across the baskets defined by the cancer types, leading to an efficient design in terms of sample size and trial duration. The treatment effects in those baskets are often heterogeneous and categorized by the cancer types being sensitive or insensitive to the treatment. Hence, the assumption of exchangeability in many existing basket trials may be violated, and there is a need to design trials without this assumption. In this paper, we simplify the constrained hierarchical Bayesian model for latent subgroups (CHBM-LS) for two classifiers to deal with the potential heterogeneity of treatment effects due to the single classifier of the cancer type. Different baskets are aggregated into subgroups using a latent subgroup modeling approach. The treatment effects are similar and exchangeable to facilitate information borrowing within each latent subgroup. Applying the simplified CHBM-LS approach to the real basket trials where baskets defined by only cancer types shows better performance than other available approaches. Further simulation study also demonstrates this CHBM-LS approach outperforms other approaches with higher statistical power and better-controlled type I error rates under various scenarios.
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