Efficient network meta-analysis: a confidence distribution approach.
Efficient network meta-analysis: a confidence distribution approach.
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DOI：
10.1016/j.stamet.2014.01.003
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发表时间：
2014-09-01
影响因子：
--
通讯作者：
Hoaglin DC
中科院分区：
文献类型：
--
作者：
Yang G;Liu D;Liu RY;Xie M;Hoaglin DC
网络荟萃分析综合了多项多治疗比较研究，同时为网络中的所有治疗提供推断。它通常可以通过从网络中的其他比较中借用证据来加强对成对比较的推断。目前的网络荟萃分析方法是从传统的成对荟萃分析或分层贝叶斯方法。本文介绍了一种新的方法，网络荟萃分析相结合的置信分布（CD）。而不是在传统的方法结合点估计从个别研究中，新的方法结合CD包含更丰富的信息比点估计，从而实现更高的效率在其推理。提出的CD方法可以有效地整合网络中的所有研究，并提供所有治疗的推断，即使个别研究只包含治疗子集的比较。通过与真实的和模拟数据集的数值研究，所提出的方法被证明优于或至少等于传统的成对荟萃分析和常用的贝叶斯分层模型。虽然贝叶斯方法可以产生与适当选择的先验相当的结果，但它对先验的选择（特别是试验间协方差结构的先验）非常敏感，这通常是主观的。CD方法是一种通用的频率论方法，并且是无先验的。此外，无论试验间协方差结构如何，它总能为所有治疗效应提供适当的推断。
Network meta-analysis synthesizes several studies of multiple treatment comparisons to simultaneously provide inference for all treatments in the network. It can often strengthen inference on pairwise comparisons by borrowing evidence from other comparisons in the network. Current network meta-analysis approaches are derived from either conventional pairwise meta-analysis or hierarchical Bayesian methods. This paper introduces a new approach for network meta-analysis by combining confidence distributions (CDs). Instead of combining point estimators from individual studies in the conventional approach, the new approach combines CDs which contain richer information than point estimators and thus achieves greater efficiency in its inference. The proposed CD approach can e ciently integrate all studies in the network and provide inference for all treatments even when individual studies contain only comparisons of subsets of the treatments. Through numerical studies with real and simulated data sets, the proposed approach is shown to outperform or at least equal the traditional pairwise meta-analysis and a commonly used Bayesian hierarchical model. Although the Bayesian approach may yield comparable results with a suitably chosen prior, it is highly sensitive to the choice of priors (especially the prior of the between-trial covariance structure), which is often subjective. The CD approach is a general frequentist approach and is prior-free. Moreover, it can always provide a proper inference for all the treatment effects regardless of the between-trial covariance structure.
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DOI：
10.1080/01621459.2012.747960
发表时间：
2013-03-01
影响因子：
3.7
作者：
Martin, Ryan;Liu, Chuanhai
通讯作者：
Liu, Chuanhai
影响因子：
4.5
作者：
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通讯作者：
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DOI：
10.1016/0197-2456(86)90046-2
发表时间：
1986-09-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
--
作者：
DERSIMONIAN, R;LAIRD, N
通讯作者：
LAIRD, N
影响因子：
4.5
作者：
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通讯作者：
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影响因子：
2
作者：
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通讯作者：
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