French National Authority for Health (HAS), Saint-Denis, France.
School of Health and Related Research, University of Sheffield, Sheffield, UK.
Pharmacoeconomics. 2019 Jan;37(1):1-6. doi: 10.1007/s40273-018-0711-9.
Models have become a nearly essential component of health technology assessment. This is because the efficacy and safety data available from clinical trials are insufficient to provide the required estimates of impact of new interventions over long periods of time and for other populations and subgroups. Despite more than five decades of use of these decision-analytic models, decision makers are still often presented with poorly validated models and thus trust in their results is impaired. Among the reasons for this vexing situation are the artificial nature of the models, impairing their validation against observable data, the complexity in their formulation and implementation, the lack of data against which to validate the model results, and the challenges of short timelines and insufficient resources. This article addresses this crucial problem of achieving models that produce results that can be trusted and the resulting requirements for validation and transparency, areas where our field is currently deficient. Based on their differing perspectives and experiences, the authors characterize the situation and outline the requirements for improvement and pragmatic solutions to the problem of inadequate validation.
模型已成为健康技术评估不可或缺的组成部分。这是因为临床试验提供的疗效和安全性数据不足以对新干预措施在长时间内对其他人群和亚组的影响进行所需的估计。尽管这些决策分析模型已经使用了五十多年，但决策者仍然经常面对验证不足的模型，因此其结果的可信度受到影响。造成这种令人烦恼的情况的原因包括模型的人为性质，这使其难以针对可观察数据进行验证，模型的制定和实施过程复杂，缺乏数据来验证模型结果，以及时间紧迫和资源不足的挑战。本文针对模型产生可信赖结果的这一关键问题，以及验证和透明度的相应要求进行了探讨，目前我们的领域在这两个方面都存在不足。基于不同的观点和经验，作者对这一情况进行了描述，并概述了改进和解决验证不足问题的务实方法。
