Constrained hierarchical Bayesian model for latent subgroups in basket trials with two classifiers. (25th October 2021)

• Record Type: Journal Article
• Authors: Takeda, Kentaro; Liu, Shufang; Rong, Alan
• Is Part Of: Statistics in medicine. Volume 41:Number 2(2022)
• Page Start: 298
• Page End: 309
• Publication Date: 2021-10-25
• Subjects: basket trials -- classifier -- constrained hierarchical Bayesian model -- heterogeneity -- latent subgroup; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9237
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract: The basket trial in oncology is a novel clinical trial design that enables the simultaneous assessment of one treatment in multiple cancer types. In addition to the usual basket classifier of the cancer types, many recent basket trials further contain other classifiers like biomarkers that potentially affect the clinical outcomes. In other words, the treatment effects in those baskets are often categorized by not only the cancer types but also the levels of other classifiers. Therefore, the assumption of exchangeability is often violated when some baskets are more sensitive to the targeted treatment, whereas others are less. In this article, we propose a constrained hierarchical Bayesian model for latent subgroups (CHBM‐LS) to deal with potential heterogeneity of treatment effects due to both the cancer type (first classifier) and another classifier (second classifier) in basket trials. Different baskets defined by multiple cancer types and multiple levels of the second classifier are aggregated into subgroups using a latent subgroup modeling approach. Within each latent subgroup, the treatment effects are similar and approximately exchangeable to borrow information. The CHBM‐LS approach evaluates the treatment effect for each basket while allowing adaptive information borrowing across the baskets by identifying latent subgroups. The simulation study shows that the CHBM‐LS approach outperforms other approaches with higher statistical power and better‐controlled type I error rates under various scenarios with heterogeneous treatment effects across baskets.