Centre for Health Economics, University of York, York, UK.
Medical Statistics Department, London School of Hygiene and Tropical Medicine, London, UK.
Value Health. 2018 Jun;21(6):715-723. doi: 10.1016/j.jval.2018.01.019. Epub 2018 Apr 25.
Empirical evidence supporting the cost-effectiveness estimates of particular health care technologies may be limited, or it may even be missing entirely. In these situations, additional information, often in the form of expert judgments, is needed to reach a decision. There are formal methods to quantify experts' beliefs, termed as structured expert elicitation (SEE), but only limited research is available in support of methodological choices. Perhaps as a consequence, the use of SEE in the context of cost-effectiveness modelling is limited.
This article reviews applications of SEE in cost-effectiveness modelling with the aim of summarizing the basis for methodological choices made in each application and recording the difficulties and challenges reported by the authors in the design, conduct, and analyses.
The methods used in each application were extracted along with the criteria used to support methodological and practical choices and any issues or challenges discussed in the text. Issues and challenges were extracted using an open field, and then categorised and grouped for reporting.
The review demonstrates considerable heterogeneity in methods used, and authors acknowledge great methodological uncertainty in justifying their choices. Specificities of the context area emerging as potentially important in determining further methodological research in elicitation are between- expert variation and its interpretation, the fact that substantive experts in the area may not be trained in quantitative subjects, that judgments are often needed on various parameter types, the need for some form of assessment of validity, and the need for more integration with behavioural research to devise relevant debiasing strategies.
This review of experiences of SEE highlights a number of specificities/constraints that can shape the development of guidance and target future research efforts in this area.
支持特定医疗保健技术成本效益估计的经验证据可能有限，甚至可能完全缺失。在这些情况下，需要额外的信息，通常以专家判断的形式，来做出决策。有量化专家意见的正式方法，称为结构化专家 elicitation（SEE），但支持方法选择的研究有限。也许因此，在成本效益建模中使用 SEE 的情况受到限制。
本文回顾了 SEE 在成本效益建模中的应用，旨在总结每个应用中所做方法选择的基础，并记录作者在设计、进行和分析中报告的困难和挑战。
提取每个应用中使用的方法，以及支持方法和实际选择的标准，以及文本中讨论的任何问题或挑战。使用开放式字段提取问题和挑战，然后进行分类和分组报告。
该综述表明，所使用的方法存在很大的异质性，作者承认在为其选择提供理由时存在很大的方法不确定性。在 elicitation 中确定进一步方法研究的潜在重要性的领域特异性包括专家之间的差异及其解释、该领域的实质性专家可能没有接受过定量学科培训的事实、往往需要对各种参数类型进行判断、需要某种形式的有效性评估，以及需要更多地与行为研究相结合，以制定相关的去偏策略。
对 SEE 经验的回顾强调了一些特殊性/限制因素，这些因素可以影响该领域指南的制定和未来研究工作的重点。
