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

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Medical Decision Making Pub Date : 2024-07-01 Epub Date: 2024-06-03 DOI:10.1177/0272989X241255047
Stijntje W Dijk, Eline Krijkamp, Natalia Kunst, Jeremy A Labrecque, Cary P Gross, Aradhana Pandit, Chia-Ping Lu, Loes E Visser, John B Wong, M G Myriam Hunink
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引用次数: 0

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.

面对不确定性做出药品审批决定:累积证据与信息价值》。
背景:COVID-19 大流行凸显了新疗法审批和进一步研究决策的关键性和复杂性。我们的研究旨在评估潜在的决策方法,这一评估对于完善未来的公共卫生危机应对措施至关重要:我们比较了药物审批和研究的 4 种决策方法:食品药品管理局的政策决定、累积荟萃分析、前瞻性信息价值(VOI)方法(使用决策时可用的信息)和参考标准(使用事后可用的信息进行回顾性 VOI 分析)。可能做出的决定包括拒绝、接受、提供紧急使用授权或仅允许在研究环境中使用新疗法。我们将提供给 COVID-19 住院患者的单克隆抗体作为案例研究,检查了 2020 年 9 月至 2021 年 12 月期间的证据,并重点关注了每种方法优化医疗结果和资源分配的能力:我们的研究结果表明,政策决策与参考标准的回顾性 VOI 方法之间存在明显差异,预计损失高达 2,690 亿美元,这表明在等待紧急用药授权期间,资源的使用未达到最佳状态。仅依靠累积荟萃分析进行决策会导致最大的预期损失,而政策方法显示的损失高达 160 亿美元,前瞻性 VOI 方法的损失最小(最多 20 亿美元):我们的研究表明,在大流行病期间,纳入 VOI 分析可能对确定研究优先次序和治疗实施决策特别有用。虽然在本案例研究中采用前瞻性 VOI 方法更受青睐,但进一步的研究应验证不同情况下的理想决策方法。本研究的发现不仅加深了我们对健康危机期间决策策略的理解,还为未来的大流行病应对措施提供了一个潜在框架:本研究回顾了大流行期间研究治疗决策参考标准(使用事后信息的回顾性 VOI)与 3 种可设想的实时方法之间的差异,表明资源的使用未达到最佳状态。在所考虑的所有前瞻性决策方法中,VOI 密切反映了参考标准,在我们的研究时限内产生的预期价值损失最小。
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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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