Bayesian random-effects meta-analysis with empirical heterogeneity priors for application in health technology assessment with very few studies

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Research Synthesis Methods Pub Date : 2023-12-28 DOI:10.1002/jrsm.1685
Jona Lilienthal, Sibylle Sturtz, Christoph Schürmann, Matthias Maiworm, Christian Röver, Tim Friede, Ralf Bender
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Abstract

In Bayesian random-effects meta-analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are adequate in the context of HTA. Therefore, a database of all relevant meta-analyses conducted by the Institute for Quality and Efficiency in Health Care (IQWiG, Germany) was constructed to derive empirical prior distributions for the heterogeneity parameter suitable for HTA. Previously, an extension to the normal-normal hierarchical model had been suggested for this purpose. For different effect measures, this extended model was applied on the database to conservatively derive a prior distribution for the heterogeneity parameter. Comparison of a Bayesian approach using the derived priors with IQWiG's current standard approach for evidence synthesis shows favorable properties. Therefore, these prior distributions are recommended for future meta-analyses in HTA settings and could be embedded into the IQWiG evidence synthesis approach in the case of very few studies.

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采用经验异质性先验的贝叶斯随机效应荟萃分析,应用于研究极少的卫生技术评估。
在贝叶斯随机效应荟萃分析中,使用弱信息先验分布对仅纳入少数研究的情况特别有益,而这正是卫生技术评估(HTA)中经常遇到的情况。文献中提供了经验先验分布的建议,但这些建议是否适用于 HTA 还不得而知。因此,我们建立了一个由医疗质量与效率研究所(IQWiG,德国)进行的所有相关荟萃分析的数据库,以推导出适合 HTA 的异质性参数的经验先验分布。在此之前,曾有人为此建议对正态-正态层次模型进行扩展。针对不同的效应量,在数据库中应用了这一扩展模型,以保守的方式得出异质性参数的先验分布。将使用推导出的先验值的贝叶斯方法与 IQWiG 目前用于证据综合的标准方法进行比较,结果显示两者具有良好的特性。因此,建议在未来的 HTA 环境中进行荟萃分析时使用这些先验分布,在研究极少的情况下,可将其嵌入 IQWiG 证据综合方法中。
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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
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