Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information

Stijntje W. Dijk, Eline Krijkamp, Natalia Kunst, Jeremy A. Labrecque, Cary P. Gross, Aradhana Pandit, Chia-Ping Lu, Loes E. Visser, John B. Wong and M. G. Myriam Hunink
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Stijntje W. Dijk: Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
Eline Krijkamp: Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
Natalia Kunst: Centre for Health Economics, University of York, York, UK
Jeremy A. Labrecque: Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
Cary P. Gross: Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale University School of Medicine, New Haven, CT, USA
Aradhana Pandit: Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
Chia-Ping Lu: Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
Loes E. Visser: Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, The Netherlands
John B. Wong: Division of Clinical Decision Making, Tufts Medical Center, Boston, USA
M. G. Myriam Hunink: Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands

Medical Decision Making, 2024, vol. 44, issue 5, 512-528

Abstract: Background The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses. Methods We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration’s policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method’s capacity to optimize health outcomes and resource allocation. Results Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion). Conclusion Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study’s findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses. Highlights This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources. Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline. This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.

Keywords: antibodies; cost-benefit analysis; COVID-19; decision support techniques; drug approval; meta-analysis; monoclonal; policy analyses (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:44:y:2024:i:5:p:512-528

DOI: 10.1177/0272989X241255047

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