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从 40 项网络荟萃分析中的间接证据中借力。

Borrowing of strength from indirect evidence in 40 network meta-analyses.

机构信息

Department of Statistics, Florida State University, Tallahassee, FL 32306, USA.

Department of Statistics, Florida State University, Tallahassee, FL 32306, USA.

出版信息

J Clin Epidemiol. 2019 Feb;106:41-49. doi: 10.1016/j.jclinepi.2018.10.007. Epub 2018 Oct 17.

DOI:10.1016/j.jclinepi.2018.10.007
PMID:30342086
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6338506/
Abstract

OBJECTIVES

Network meta-analysis (NMA) is increasingly being used to synthesize direct and indirect evidence and help decision makers simultaneously compare multiple treatments. We empirically evaluate the incremental gain in precision achieved by incorporating indirect evidence in NMAs.

STUDY DESIGN AND SETTING

We performed both network and pairwise meta-analyses on 40 published data sets of multiple-treatment comparisons. Their results were compared using the recently proposed borrowing of strength (BoS) statistic, which quantifies the percentage reduction in the uncertainty of the effect estimate when adding indirect evidence to an NMA.

RESULTS

We analyzed 915 possible treatment comparisons, from which 484 (53%) had no direct evidence (BoS = 100%). In 181 comparisons with only one study contributing direct evidence, NMAs resulted in reduced precision (BoS < 0) and no appreciable improvements in precision (0 < BoS < 30%) for 104 (57.5%) and 23 (12.7%) comparisons, respectively. In 250 comparisons with at least two studies contributing direct evidence, NMAs provided increased precision with BoS ≥ 30% for 166 (66.4%) comparisons.

CONCLUSION

Although NMAs have the potential to provide more precise results than those only based on direct evidence, the incremental gain may reliably occur only when at least two head-to-head studies are available and treatments are well connected. Researchers should routinely report and compare the results from both network and pairwise meta-analyses.

摘要

目的

网络荟萃分析(NMA)越来越多地被用于综合直接和间接证据,帮助决策者同时比较多种治疗方法。我们通过实证评估在 NMA 中纳入间接证据所获得的精度增益。

研究设计和设置

我们对 40 个多治疗比较的已发表数据集进行了网络荟萃分析和两两荟萃分析。使用最近提出的借用强度(BoS)统计量来比较它们的结果,该统计量量化了当将间接证据添加到 NMA 中时对效应估计不确定性的减少百分比。

结果

我们分析了 915 种可能的治疗比较,其中 484 种(53%)没有直接证据(BoS=100%)。在 181 种仅有一项研究提供直接证据的比较中,NMA 导致精度降低(BoS<0),并且对于 104 种(57.5%)和 23 种(12.7%)比较,精度没有明显提高(0<BoS<30%)。在 250 种至少有两项研究提供直接证据的比较中,NMA 为 166 种(66.4%)比较提供了 BoS≥30%的精度增益。

结论

尽管 NMA 有可能提供比仅基于直接证据更精确的结果,但只有当至少有两项头对头研究可用且治疗方法相互关联时,才能可靠地获得增量增益。研究人员应定期报告并比较网络荟萃分析和两两荟萃分析的结果。

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