A Comprehensive Bayesian Approach to Quantify and Compare Benefit-Risk of Medical Products.
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| Title: | A Comprehensive Bayesian Approach to Quantify and Compare Benefit-Risk of Medical Products. |
|---|---|
| Authors: | Mukhopadhyay, Saurabh1 (AUTHOR) saurabh.mukhopadhyay@abbvie.com, Payne, Richard D.2 (AUTHOR) |
| Source: | Statistics in Biopharmaceutical Research. Jul-Sep2026, Vol. 18 Issue 3, p481-491. 11p. |
| Subjects: | Bayesian analysis, Hierarchical Bayes model, Risk assessment, Treatment effectiveness, Stakeholder analysis, Clinical trials, Health products, Quantitative research |
| Abstract: | For a new medical product, it is necessary to show that its benefits outweigh its risks. Quantitative benefit-risk assessments (qBRA) aim to establish clear, transparent, and evidence-based criteria to compare the benefit-risk profiles of different treatments. Two significant challenges in the implementation of qBRA are how to translate preferences into numeric scores and how to combine them with observed outcomes of treatments. One of the key challenges has been addressed by the hierarchical Bayes benefit-risk (HBBR) method (Mukhopadhyay et al.), which offers a transparent and evidence-based approach to estimate preferences using tradeoff data from stakeholders. However, a critical gap remains in how to effectively integrate those estimated preferences with observed outcomes. In this article, an overall benefit-risk utility score of a treatment is introduced using a Bayesian framework that combines estimated preferences with treatment outcomes. The methodology accounts for uncertainty in both the preferences elicited from stakeholders and the outcomes from clinical trial data. It also allows for stochastic comparisons of overall preferences between two or more treatments. The methodology is agnostic to choice of Bayesian model to estimate the treatment outcomes from clinical trial data and can be applied at any stage of the medical product lifecycle, especially during clinical development. Simulated data are used to illustrate the method. [ABSTRACT FROM AUTHOR] |
| Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 195621050 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Comprehensive Bayesian Approach to Quantify and Compare Benefit-Risk of Medical Products. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mukhopadhyay%2C+Saurabh%22">Mukhopadhyay, Saurabh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> saurabh.mukhopadhyay@abbvie.com</i><br /><searchLink fieldCode="AR" term="%22Payne%2C+Richard+D%2E%22">Payne, Richard D.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Statistics+in+Biopharmaceutical+Research%22">Statistics in Biopharmaceutical Research</searchLink>. Jul-Sep2026, Vol. 18 Issue 3, p481-491. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Hierarchical+Bayes+model%22">Hierarchical Bayes model</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Stakeholder+analysis%22">Stakeholder analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+trials%22">Clinical trials</searchLink><br /><searchLink fieldCode="DE" term="%22Health+products%22">Health products</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: For a new medical product, it is necessary to show that its benefits outweigh its risks. Quantitative benefit-risk assessments (qBRA) aim to establish clear, transparent, and evidence-based criteria to compare the benefit-risk profiles of different treatments. Two significant challenges in the implementation of qBRA are how to translate preferences into numeric scores and how to combine them with observed outcomes of treatments. One of the key challenges has been addressed by the hierarchical Bayes benefit-risk (HBBR) method (Mukhopadhyay et al.), which offers a transparent and evidence-based approach to estimate preferences using tradeoff data from stakeholders. However, a critical gap remains in how to effectively integrate those estimated preferences with observed outcomes. In this article, an overall benefit-risk utility score of a treatment is introduced using a Bayesian framework that combines estimated preferences with treatment outcomes. The methodology accounts for uncertainty in both the preferences elicited from stakeholders and the outcomes from clinical trial data. It also allows for stochastic comparisons of overall preferences between two or more treatments. The methodology is agnostic to choice of Bayesian model to estimate the treatment outcomes from clinical trial data and can be applied at any stage of the medical product lifecycle, especially during clinical development. Simulated data are used to illustrate the method. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Statistics in Biopharmaceutical Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/19466315.2025.2547857 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 481 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Hierarchical Bayes model Type: general – SubjectFull: Risk assessment Type: general – SubjectFull: Treatment effectiveness Type: general – SubjectFull: Stakeholder analysis Type: general – SubjectFull: Clinical trials Type: general – SubjectFull: Health products Type: general – SubjectFull: Quantitative research Type: general Titles: – TitleFull: A Comprehensive Bayesian Approach to Quantify and Compare Benefit-Risk of Medical Products. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mukhopadhyay, Saurabh – PersonEntity: Name: NameFull: Payne, Richard D. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul-Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19466315 Numbering: – Type: volume Value: 18 – Type: issue Value: 3 Titles: – TitleFull: Statistics in Biopharmaceutical Research Type: main |
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