Conducting Indirect-Treatment-Comparison and Network-Meta-Analysis Studies: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 2
Conducting Indirect-Treatment-Comparison and Network-Meta-Analysis Studies: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 2
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DOI：
10.1016/j.jval.2011.01.011
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发表时间：
2011-06-01
期刊：
影响因子：
4.5
通讯作者：
Barrett, Annabel
中科院分区：
文献类型：
--
作者：
Hoaglin, David C.;Hawkins, Neil;Barrett, Annabel
基于证据的医疗保健决策需要比较所有相关的竞争干预措施。在缺乏涉及所有感兴趣的治疗的直接比较的随机对照试验的情况下，间接治疗比较和网络荟萃分析为明智地选择最佳治疗提供了有用的证据。混合治疗比较是网络荟萃分析的一种特殊情况，它将直接证据和间接证据结合起来进行特定的成对比较，从而比传统荟萃分析合成了更大份额的可用证据。国际药物经济学和结果研究学会间接治疗比较良好研究实践工作组的这份报告为进行网络荟萃分析的技术方面提供了指导（我们对该术语的使用包括大多数涉及证据网络背景下荟萃分析的方法）。我们首先讨论发展证据网络的策略。接下来我们简要回顾一下网络荟萃分析的假设。然后我们重点关注数据的统计分析：目标、模型（固定效应和随机效应）、频率论与贝叶斯方法以及模型验证。清单强调了网络荟萃分析的关键组成部分，并通过大量示例说明了间接治疗比较（频率论和贝叶斯方法）和网络荟萃分析。进一步的部分讨论了未来研究的八个关键领域。
Evidence-based health care decision making requires comparison of all relevant competing interventions. In the absence of randomized controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best treatment(s). Mixed treatment comparisons, a special case of network meta-analysis, combine direct evidence and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than traditional meta-analysis. This report from the International Society for Pharmacoeconomics and Outcomes Research Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on technical aspects of conducting network meta-analyses (our use of this term includes most methods that involve meta-analysis in the context of a network of evidence). We start with a discussion of strategies for developing networks of evidence. Next we briefly review assumptions of network meta-analysis. Then we focus on the statistical analysis of the data: objectives, models (fixed-effects and random-effects), frequentist versus Bayesian approaches, and model validation. A checklist highlights key components of network meta-analysis, and substantial examples illustrate indirect treatment comparisons (both frequentist and Bayesian approaches) and network meta-analysis. A further section discusses eight key areas for future research.