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Evaluating treatments in rare indications warrants a Bayesian approach

Evaluating efficacy and real-world effectiveness for novel therapies targeting rare mutations or patient subpopulations with unmet needs is a growing challenge in health economics and outcomes research (HEOR). In these settings it may be difficult to recruit enough patients to run adequately powered...

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Autores principales: Mackay, Emma K., Springford, Aaron
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547867/
https://www.ncbi.nlm.nih.gov/pubmed/37799966
http://dx.doi.org/10.3389/fphar.2023.1249611
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author Mackay, Emma K.
Springford, Aaron
author_facet Mackay, Emma K.
Springford, Aaron
author_sort Mackay, Emma K.
collection PubMed
description Evaluating efficacy and real-world effectiveness for novel therapies targeting rare mutations or patient subpopulations with unmet needs is a growing challenge in health economics and outcomes research (HEOR). In these settings it may be difficult to recruit enough patients to run adequately powered randomized clinical trials, resulting in greater reliance on single-arm trials or basket trial designs. Additionally, evidence networks for performing network meta-analysis may be sparse or disconnected when comparing available treatments in narrower patient populations. These challenges create an increased need for use of appropriate methods for handling small sample sizes, structural modelling assumptions and more nuanced decision rules to arrive at “best-available evidence” on comparative and non-comparative efficacy/effectiveness. We advocate for greater use of Bayesian methods to address these challenges as they can facilitate efficient and transparent borrowing of information across varied data sources under flexible modelling assumptions, probabilistic sensitivity analysis to assess model assumptions, and more nuanced decision-making where limited power reduces the utility of classical frequentist hypothesis testing. We illustrate how Bayesian methods have been recently used to overcome several challenges of rare indications in HEOR, including approaches to borrowing information from external data sources, evaluation of efficacy in basket trials, and incorporating non-randomized studies into network meta-analysis. Lastly, we provide several recommendations for HEOR practitioners on appropriate use of Bayesian methods to address challenges in the rare disease setting.
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spelling pubmed-105478672023-10-05 Evaluating treatments in rare indications warrants a Bayesian approach Mackay, Emma K. Springford, Aaron Front Pharmacol Pharmacology Evaluating efficacy and real-world effectiveness for novel therapies targeting rare mutations or patient subpopulations with unmet needs is a growing challenge in health economics and outcomes research (HEOR). In these settings it may be difficult to recruit enough patients to run adequately powered randomized clinical trials, resulting in greater reliance on single-arm trials or basket trial designs. Additionally, evidence networks for performing network meta-analysis may be sparse or disconnected when comparing available treatments in narrower patient populations. These challenges create an increased need for use of appropriate methods for handling small sample sizes, structural modelling assumptions and more nuanced decision rules to arrive at “best-available evidence” on comparative and non-comparative efficacy/effectiveness. We advocate for greater use of Bayesian methods to address these challenges as they can facilitate efficient and transparent borrowing of information across varied data sources under flexible modelling assumptions, probabilistic sensitivity analysis to assess model assumptions, and more nuanced decision-making where limited power reduces the utility of classical frequentist hypothesis testing. We illustrate how Bayesian methods have been recently used to overcome several challenges of rare indications in HEOR, including approaches to borrowing information from external data sources, evaluation of efficacy in basket trials, and incorporating non-randomized studies into network meta-analysis. Lastly, we provide several recommendations for HEOR practitioners on appropriate use of Bayesian methods to address challenges in the rare disease setting. Frontiers Media S.A. 2023-09-20 /pmc/articles/PMC10547867/ /pubmed/37799966 http://dx.doi.org/10.3389/fphar.2023.1249611 Text en Copyright © 2023 Mackay and Springford. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Mackay, Emma K.
Springford, Aaron
Evaluating treatments in rare indications warrants a Bayesian approach
title Evaluating treatments in rare indications warrants a Bayesian approach
title_full Evaluating treatments in rare indications warrants a Bayesian approach
title_fullStr Evaluating treatments in rare indications warrants a Bayesian approach
title_full_unstemmed Evaluating treatments in rare indications warrants a Bayesian approach
title_short Evaluating treatments in rare indications warrants a Bayesian approach
title_sort evaluating treatments in rare indications warrants a bayesian approach
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547867/
https://www.ncbi.nlm.nih.gov/pubmed/37799966
http://dx.doi.org/10.3389/fphar.2023.1249611
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