Bayesian hierarchical classification and information sharing for clinical trials with subgroups and binary outcomes

Chen, Nan; Lee, John

doi:10.1002/bimj.201700275

Cited by 20 publications

(22 citation statements)

References 33 publications

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“…Another important method of borrowing information is BHM, 21 proposed by Berry, using variance in a hierarchical model to control the extent of borrowing. BHM has been developed into different variants, such as the calibrated Bayesian hierarchical model (CBHM), 22 Bayesian hierarchical classification and information sharing (BaCIS), 23 and Bayesian cluster hierarchical model (BCHM). 24 Although MEMs are not variants of BHM, MEMs have been extended to basket trials.…”

Section: Methodsmentioning

confidence: 99%

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

Chen

Zhang

et al. 2022

JCO Precision Oncology

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PURPOSE With deeper insight into precision medicine, more innovative oncology trial designs have been proposed to contribute to the characteristics of novel antitumor drugs. Bayesian information borrowing is an indispensable part of these designs, which shows great advantages in improving the efficiency of clinical trials. Bayesian methods provide an effective framework when incorporating information. However, the key point lies in how to choose an appropriate method for complex oncology clinical trials. METHODS We divided the borrowing information scenarios into concurrent and nonconcurrent scenarios according to whether the data to be borrowed are observed at the same time as in the current trial or not. Then, we provided an overview of the methods in each scenario. Performance comparison of different methods is carried out with regard to the type I error and power. RESULTS As demonstrated by the simulation results in each borrowing scenario, the Bayesian hierarchical model and its extensions are more appropriate for concurrent borrowing. The simulation results demonstrate that the Bayesian hierarchical model shows great advantages when the arms are homogeneous. However, such a method should be adopted with caution when heterogeneity exists. We recommend the other methods, considering heterogeneity. Borrow information from informative priors is more suggested for nonconcurrent borrowing scenarios. Multisource exchangeability models are more suitable for multiple historical trials, while meta-analytic-predictive prior should be carefully applied. CONCLUSION Bayesian information borrowing is useful and can improve the efficiency of clinical trial designs. However, we should carefully choose an appropriate information borrowing method when facing a practical innovative oncology trial, as an appropriate method is essential to provide ideal design performance.

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Section: Methodsmentioning

confidence: 99%

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

Chen

Zhang

et al. 2022

JCO Precision Oncology

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“…Hierarchical models devised to share information across indications in relation to estimates of statistical exchangeability have been proposed by various authors 18,[35][36][37][38] with the intention of increasing statistical power while controlling bias. Freidlin and Korn 39 evaluated the statistical properties of designs with subpopulation analysis using single-source Bayesian hierarchical models.…”

Section: Design Criteria and Operating Characteristicsmentioning

confidence: 99%

Basket Designs: Statistical Considerations for Oncology Trials

et al. 2019

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Progress in the areas of genomics, disease pathways, and drug discovery has advanced into clinical and translational cancer research. The latest innovations in clinical trials have followed with master protocols, which are defined by inclusive eligibility criteria and devised to interrogate multiple therapies for a given tumor histology and/or multiple histologies for a given therapy under one protocol. The use of master protocols for oncology has become more common with the desire to improve the efficiency of clinical research and accelerate overall drug development. Basket trials have been devised to ascertain the extent to which a treatment strategy offers benefit to various patient subpopulations defined by a common molecular target. Conventionally conducted within the phase II setting, basket designs have become popular as drug developers seek to effectively evaluate and identify preliminary efficacy signals among clinical indications identified as promising in preclinical study. This article reviews basket trial designs in oncology settings and discusses several issues that arise with their design and analysis.

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“…Chen and Lee proposed a completely data-driven methodology to allow for hierarchical information borrowing after clustering treatment arms into subgroups. 30 HCOMBS is distinct and novel compared to this methodology in the following three ways. First, HCOMBS has the ability to simultaneously do clustering and hierarchical modeling in one step.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Bayesian clustering design of multiple biomarker subgroups (HCOMBS)

Kang

Coffey

Smith

et al. 2021

Statistics in Medicine

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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.

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Bayesian hierarchical classification and information sharing for clinical trials with subgroups and binary outcomes

Cited by 20 publications

References 33 publications

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

Basket Designs: Statistical Considerations for Oncology Trials

Hierarchical Bayesian clustering design of multiple biomarker subgroups (HCOMBS)

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