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Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps—impeding timely pre...

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Autores principales: Courcelles, Eulalie, Boissel, Jean-Pierre, Massol, Jacques, Klingmann, Ingrid, Kahoul, Riad, Hommel, Marc, Pham, Emmanuel, Kulesza, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907708/
https://www.ncbi.nlm.nih.gov/pubmed/35281671
http://dx.doi.org/10.3389/fmedt.2022.810315
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author Courcelles, Eulalie
Boissel, Jean-Pierre
Massol, Jacques
Klingmann, Ingrid
Kahoul, Riad
Hommel, Marc
Pham, Emmanuel
Kulesza, Alexander
author_facet Courcelles, Eulalie
Boissel, Jean-Pierre
Massol, Jacques
Klingmann, Ingrid
Kahoul, Riad
Hommel, Marc
Pham, Emmanuel
Kulesza, Alexander
author_sort Courcelles, Eulalie
collection PubMed
description Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps—impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.
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spelling pubmed-89077082022-03-11 Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models? Courcelles, Eulalie Boissel, Jean-Pierre Massol, Jacques Klingmann, Ingrid Kahoul, Riad Hommel, Marc Pham, Emmanuel Kulesza, Alexander Front Med Technol Medical Technology Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps—impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders. Frontiers Media S.A. 2022-02-24 /pmc/articles/PMC8907708/ /pubmed/35281671 http://dx.doi.org/10.3389/fmedt.2022.810315 Text en Copyright © 2022 Courcelles, Boissel, Massol, Klingmann, Kahoul, Hommel, Pham and Kulesza. 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 Medical Technology
Courcelles, Eulalie
Boissel, Jean-Pierre
Massol, Jacques
Klingmann, Ingrid
Kahoul, Riad
Hommel, Marc
Pham, Emmanuel
Kulesza, Alexander
Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?
title Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?
title_full Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?
title_fullStr Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?
title_full_unstemmed Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?
title_short Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?
title_sort solving the evidence interpretability crisis in health technology assessment: a role for mechanistic models?
topic Medical Technology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8907708/
https://www.ncbi.nlm.nih.gov/pubmed/35281671
http://dx.doi.org/10.3389/fmedt.2022.810315
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