Minimal Modeling Approaches to Value of Information Analysis for Health Research
Minimal Modeling Approaches to Value of Information Analysis for Health Research
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DOI：
10.1177/0272989x11412975
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发表时间：
2011-11-01
影响因子：
3.6
通讯作者：
Basu, Anirban
中科院分区：
文献类型：
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作者：
Meltzer, David O.;Hoomans, Ties;Basu, Anirban
信息价值（VOI）技术可以提供临床研究预期收益的估计，可以为这些研究的设计和优先级决策提供信息。大多数VOI研究使用决策分析模型来表征干预措施对健康结果影响的不确定性，但构建此类模型的复杂性可能对VOI的一些实际应用构成障碍。然而，由于一些临床研究可以直接表征健康结果的不确定性，有时可能只需要最小的建模就可以进行VOI分析。本文1)开发了一个框架来定义和分类VOI的最小建模方法，2)回顾了应用最小建模方法的现有VOI研究，3)说明并讨论了最小建模在两种新的临床应用中的应用，该方法似乎非常适合，因为具有综合结果的临床试验提供了对结果不确定性的初步估计。作者得出结论，对VOI的最小建模方法可以很容易地应用于某些情况下，以估计临床研究的预期收益。
Value of information (VOI) techniques can provide estimates of the expected benefits from clinical research studies that can inform decisions about the design and priority of those studies. Most VOI studies use decision-analytic models to characterize the uncertainty of the effects of interventions on health outcomes, but the complexity of constructing such models can pose barriers to some practical applications of VOI. However, because some clinical studies can directly characterize uncertainty in health-outcomes, it may sometimes be possible to perform VOI analysis with only minimal modeling. This article 1) develops a framework to define and classify minimal modeling approaches to VOI, 2) reviews existing VOI studies that apply minimal modeling approaches, and 3) illustrates and discusses the application of the minimal modeling to 2 new clinical applications to which the approach appears well suited because clinical trials with comprehensive outcomes provide preliminary estimates of the uncertainty in outcomes. The authors conclude that minimal modeling approaches to VOI can be readily applied in some instances to estimate the expected benefits of clinical research.