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决策模型中证据的使用:对1997年以来英国卫生技术评估的评估

Use of evidence in decision models: an appraisal of health technology assessments in the UK since 1997.

作者信息

Cooper Nicola, Coyle Douglas, Abrams Keith, Mugford Miranda, Sutton Alexander

机构信息

Centre for Biostatistics and Genetic Epidemiology, Department of Health Sciences, University of Leicester, UK.

出版信息

J Health Serv Res Policy. 2005 Oct;10(4):245-50. doi: 10.1258/135581905774414187.

Abstract

OBJECTIVES

To review the sources and quality of evidence used in the development of economic decision models in health technology assessments (HTAs).

METHODS

All economic decision models developed as part of the NHS Research and Development HTA Programme between 1997 and 2003 were reviewed. Quality of evidence was assessed using a hierarchy of data sources developed for economic analyses.

RESULTS

Decision models are parameterized using diverse sources of evidence (e.g. randomized controlled trials, observational studies, expert opinion). Evidence on the main clinical effect was mostly identified and quality assessed as part of the companion systematic review/meta-analysis of the HTA and therefore reported in a transparent and reproducible way. For the other model inputs (i.e. adverse events, baseline clinical data, resource use and utilities), the search strategies for identifying relevant evidence were rarely made explicit and in a number of reports the sources of specific evidence were unclear due to poor reporting.

CONCLUSIONS

A more formal and replicable approach to identification and assessment of quality of model inputs is required to reduce the 'black box' nature of decision models, and lead to less scepticism regarding model outputs.

摘要

目标

回顾卫生技术评估(HTA)中经济决策模型开发所使用证据的来源和质量。

方法

对1997年至2003年间作为英国国家医疗服务体系研发HTA计划一部分而开发的所有经济决策模型进行回顾。使用为经济分析制定的数据来源层次结构评估证据质量。

结果

决策模型使用多种证据来源(如随机对照试验、观察性研究、专家意见)进行参数化。关于主要临床效果的证据大多作为HTA配套系统评价/荟萃分析的一部分被识别和质量评估,因此以透明且可重复的方式报告。对于其他模型输入(即不良事件、基线临床数据、资源使用和效用),识别相关证据的检索策略很少明确,并且在一些报告中,由于报告不佳,具体证据的来源不清楚。

结论

需要一种更正式且可重复的方法来识别和评估模型输入的质量,以减少决策模型的“黑箱”性质,并减少对模型输出的怀疑。

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