Hierarchical Bayesian clustering design of multiple biomarker subgroups (HCOMBS)

Abstract

Given the Food and Drug Administration's (FDA's) acceptance of master protocol designs in recent guidance documents, the oncology field is rapidly moving to address the paradigm shift to molecular subtype focused studies. Identifying new “marker‐based” treatments requires new methodologies to address the growing demand to conduct clinical trials in smaller molecular subpopulations, identify effective treatment and marker interactions, and control for false positives. We introduce our methodology, Hierarchical Bayesian Clustering Design of Multiple Biomarker Subgroups (HCOMBS), a two‐stage umbrella Phase II design with effect size clustering and information borrowing across multiple biomarker‐treatment pairs. HCOMBS was designed to reduce required sample size, differentiate between varying effect sizes, and control for operating characteristics in the multi‐arm setting. When compared to independently applied Simon's Optimal two‐stage design, we showed through simulations that HCOMBS required less participants per treatment arm with a well‐controlled family‐wise error rate and desirable marginal power. Additionally, HCOMBS features a statistical approach that simultaneously conducts clustering and hypothesis testing in one step. We also applied the proposed design on the alliance brain metastases umbrella trial.

Bayesian local exchangeability design for phase II basket trials

“…Alternative methods based on clustering or mixture models can be applied when there is a large number of baskets. 15,17,18 ACKNOWLEDGEMENT We thank Dr. Alexander M. Kaizer for the very helpful comments and suggestions on this article.…”

“…A variety of statistical strategies have been proposed to improve the accuracy and efficiency of basket trials. [9][10][11][12][13][14][15][16][17][18] The Bayesian hierarchical model approach (BHM) formulated by Thall et al 9 and Berry et al 11 aims to improve the efficiency of basket trials by enabling information borrowing across baskets. The validity of the BHM relies on the assumption of single source exchangeability (SSE), which assumes the response rates from different baskets arise from a common parent distribution defined by trial-level parameters.…”

“…A variety of statistical strategies have been proposed to improve the accuracy and efficiency of basket trials 9‐18 . The Bayesian hierarchical model approach (BHM) formulated by Thall et al 9 and Berry et al 11 aims to improve the efficiency of basket trials by enabling information borrowing across baskets.…”

Bayesian local exchangeability design for phase II basket trials

“…A variety of statistical strategies have been proposed to improve the accuracy and efficiency of basket trials. [9][10][11][12][13][14][15][16][17][18] Bayesian hierarchical models (BHM) formulated by Thall et al [9] and Berry et al [11] improve the efficiency of basket trials by enabling information borrowing across baskets. The validity of the BHM relies on the assumption of single source exchangeability (SSE).…”

“…Zhou et al [17] infers the number of subgroups and subgroup memberships using a Dirichlet process mixture model. Kang et al propose a hierarchical Bayesian clustering design that clusters arms into either active or inactive subgroup [18]. These aforementioned approaches are computationally demanding and are difficult to implement due to the complexities involved in model and prior specifications.…”