Methods in health service research - An introduction to bayesian methods in health technology assessment
Methods in health service research - An introduction to bayesian methods in health technology assessment
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DOI：
10.1136/bmj.319.7208.508
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发表时间：
1999-08-21
影响因子：
--
通讯作者：
Abrams, KR
中科院分区：
文献类型：
--
作者：
Spiegelhalter, DJ;Myles, JP;Abrams, KR
贝叶斯定理源于1763年汤布里奇·威尔斯的一位不墨守成规的牧师托马斯·贝耶斯在死后发表的一篇文章。虽然它在概率论中给出了一个简单而没有争议的结果，但该定理的具体用法在两个多世纪以来一直是相当有争议的主题。近年来，出现了一种更加平衡和务实的观点，本文回顾了当前关于贝叶斯方法在卫生技术评估中的价值的思考。贝叶斯方法在卫生技术评估中的简明定义尚未建立，但我们建议如下：在卫生技术评估的设计、监测、分析、解释和报告中明确量化使用外部证据。这种方法承认，对一项新技术的益处的判断很少会仅仅基于单一研究的结果，而应该综合来自多个来源的证据--例如，试点研究、类似干预试验，甚至是对研究结果的概括性的主观判断。贝叶斯观点导致了一种据称比传统方法更灵活和更符合伦理的临床试验方法，1以及处理多个子研究的优雅方法--例如，同时评估一种治疗对许多子组的影响。2支持者还认为，贝叶斯方法允许以最适合于特定于患者的决定和影响公共政策的决定的形式提供结论。3.
Bayes’s theorem arose from a posthumous publication in 1763 by Thomas Bayes, a non-conformist minister from Tunbridge Wells. Although it gives a simple and uncontroversial result in probability theory, specific uses of the theorem have been the subject of considerable controversy for more than two centuries. In recent years a more balanced and pragmatic perspective has emerged, and in this paper we review current thinking on the value of the Bayesian approach to health technology assessment.A concise definition of bayesian methods in health technology assessment has not been established, but we suggest the following: the explicit quantitative use of external evidence in the design, monitoring, analysis, interpretation, and reporting of a health technology assessment. This approach acknowledges that judgments about the benefits of a new technology will rarely be based solely on the results of a single study but should synthesise evidence from multiple sources—for example, pilot studies, trials of similar interventions, and even subjective judgments about the generalisability of the study’s results. A bayesian perspective leads to an approach to clinical trials that is claimed to be more flexible and ethical than traditional methods, 1 and to elegant ways of handling multiple substudies—for example, when simultaneously estimating the effects of a treatment on many subgroups. 2 Proponents have also argued that a bayesian approach allows conclusions to be provided in a form that is most suitable for decisions specific to patients and decisions affecting public policy. 3