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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 19466315 |
| DOI: | 10.1080/19466315.2025.2547857 |
