A variance shrinkage method improves arm-based Bayesian network meta-analysis.
A variance shrinkage method improves arm-based Bayesian network meta-analysis.
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一种差异方法改善了基于ARM的贝叶斯网络荟萃分析。
DOI：
10.1177/0962280220945731
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发表时间：
2021-01
影响因子：
2.3
通讯作者：
Chu H
中科院分区：
文献类型：
--
作者：
Wang Z;Lin L;Hodges JS;MacLehose R;Chu H
与传统的成对荟萃分析相比，网络荟萃分析(NMA)是在多个治疗的系统评价中结合直接和间接证据以改进估计的常用工具。与基于对比的NMA方法不同，NMA方法侧重于估计相对影响，如赔率比，而基于ARM(AB)的NMA方法可以估计绝对风险和其他影响，这在医学和公共卫生方面可以提供更多信息。然而，在NMA中，涉及每种治疗的临床研究的数量往往很少，这导致在AB-NMA方法中，当在不相等的方差假设下使用非信息或弱信息的先验时，特定于治疗的方差估计不稳定。其他假设，例如所有处理的相等(即均匀)方差，可以用来解决这个问题，但这样的假设可能不适当地强烈。本文介绍了AB-NMA的一种方差收缩方法。具体地说，我们假设不同的处理方差共享一个具有未知超参数的共同先验。这一假设弱于均方差假设，并通过以数据依赖的方式缩小方差来改进估计。我们通过重新分析有组织的卒中住院护理干预的NMA来说明方差收缩方法的优势。最后，综合仿真考察了不同的方差假设对统计推断的影响，仿真结果表明，方差收缩方法能更好地估计对数赔率比和绝对风险。
Network meta-analysis (NMA) is a commonly used tool to combine direct and indirect evidence in systematic reviews of multiple treatments to improve estimation compared to traditional pairwise meta-analysis. Unlike the contrast-based NMA approach, which focuses on estimating relative effects such as odds ratios, the arm-based (AB) NMA approach can estimate absolute risks and other effects, which are arguably more informative in medicine and public health. However, the number of clinical studies involving each treatment is often small in an NMA, leading to unstable treatment-specific variance estimates in the AB-NMA approach when using non- or weakly-informative priors under an unequal variance assumption. Additional assumptions, such as equal (i.e., homogeneous) variances for all treatments, may be used to remedy this problem but such assumptions may be inappropriately strong. This article introduces a variance shrinkage method for an AB-NMA. Specifically, we assume different treatment variances share a common prior with unknown hyper-parameters. This assumption is weaker than the homogeneous-variance assumption and improves estimation by shrinking the variances in a data-dependent way. We illustrate the advantages of the variance shrinkage method by re-analyzing an NMA of organized inpatient care interventions for stroke. Finally, comprehensive simulations investigate the impact of different variance assumptions on statistical inference, and simulation results show that the variance shrinkage method provides better estimation for log odds ratios and absolute risks.
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