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IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy

BACKGROUND: Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult...

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Detalles Bibliográficos
Autores principales: Chen, Xin, Zhang, Jingyi, Jiang, Liyun, Yan, Fangrong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10026491/
https://www.ncbi.nlm.nih.gov/pubmed/36941537
http://dx.doi.org/10.1186/s12874-023-01877-w
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author Chen, Xin
Zhang, Jingyi
Jiang, Liyun
Yan, Fangrong
author_facet Chen, Xin
Zhang, Jingyi
Jiang, Liyun
Yan, Fangrong
author_sort Chen, Xin
collection PubMed
description BACKGROUND: Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult for efficacy evaluation in clinical trials. Therefore, it is necessary to develop innovative clinical trial designs to explore the efficacy of targeted combination therapy in different subgroups and identify patients who are more likely to benefit from the investigational combination therapy. METHODS: We propose a statistical tool called ‘IBIS’ to Identify BIomarker-based Subgroups and apply it to the enrichment design framework. The IBIS contains three main elements: subgroup division, efficacy evaluation and subgroup identification. We first enumerate all possible subgroup divisions based on biomarker levels. Then, Jensen–Shannon divergence is used to distinguish high-efficacy and low-efficacy subgroups, and Bayesian hierarchical model (BHM) is employed to borrow information within these two subsets for efficacy evaluation. Regarding subgroup identification, a hypothesis testing framework based on Bayes factors is constructed. This framework also plays a key role in go/no-go decisions and enriching specific population. Simulation studies are conducted to evaluate the proposed method. RESULTS: The accuracy and precision of IBIS could reach a desired level in terms of estimation performance. In regard to subgroup identification and population enrichment, the proposed IBIS has superior and robust characteristics compared with traditional methods. An example of how to obtain design parameters for an adaptive enrichment design under the IBIS framework is also provided. CONCLUSIONS: IBIS has the potential to be a useful tool for biomarker-based subgroup identification and population enrichment in clinical trials of targeted combination therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01877-w.
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spelling pubmed-100264912023-03-21 IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy Chen, Xin Zhang, Jingyi Jiang, Liyun Yan, Fangrong BMC Med Res Methodol Research BACKGROUND: Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult for efficacy evaluation in clinical trials. Therefore, it is necessary to develop innovative clinical trial designs to explore the efficacy of targeted combination therapy in different subgroups and identify patients who are more likely to benefit from the investigational combination therapy. METHODS: We propose a statistical tool called ‘IBIS’ to Identify BIomarker-based Subgroups and apply it to the enrichment design framework. The IBIS contains three main elements: subgroup division, efficacy evaluation and subgroup identification. We first enumerate all possible subgroup divisions based on biomarker levels. Then, Jensen–Shannon divergence is used to distinguish high-efficacy and low-efficacy subgroups, and Bayesian hierarchical model (BHM) is employed to borrow information within these two subsets for efficacy evaluation. Regarding subgroup identification, a hypothesis testing framework based on Bayes factors is constructed. This framework also plays a key role in go/no-go decisions and enriching specific population. Simulation studies are conducted to evaluate the proposed method. RESULTS: The accuracy and precision of IBIS could reach a desired level in terms of estimation performance. In regard to subgroup identification and population enrichment, the proposed IBIS has superior and robust characteristics compared with traditional methods. An example of how to obtain design parameters for an adaptive enrichment design under the IBIS framework is also provided. CONCLUSIONS: IBIS has the potential to be a useful tool for biomarker-based subgroup identification and population enrichment in clinical trials of targeted combination therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01877-w. BioMed Central 2023-03-20 /pmc/articles/PMC10026491/ /pubmed/36941537 http://dx.doi.org/10.1186/s12874-023-01877-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chen, Xin
Zhang, Jingyi
Jiang, Liyun
Yan, Fangrong
IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy
title IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy
title_full IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy
title_fullStr IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy
title_full_unstemmed IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy
title_short IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy
title_sort ibis: identify biomarker-based subgroups with a bayesian enrichment design for targeted combination therapy
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10026491/
https://www.ncbi.nlm.nih.gov/pubmed/36941537
http://dx.doi.org/10.1186/s12874-023-01877-w
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