The hierarchical metaregression approach and learning from clinical evidence
The hierarchical metaregression approach and learning from clinical evidence
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DOI：
10.1002/bimj.201700266
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发表时间：
2019-05-01
影响因子：
1.7
通讯作者：
Verde, Pablo Emilio
中科院分区：
文献类型：
--
作者：
Verde, Pablo Emilio
分层元回归（hierarchical metaregression，HMR）方法是一种多参数贝叶斯元分析方法，它通过显式地对元分析中的数据收集过程进行建模，推广了标准的混合效应模型。HMR允许调查实验结果的潜在外部效度，以及评估系统综述中包含的研究的内部效度。HMR自动识别提出相互矛盾证据的研究，并降低其在荟萃分析中的影响力。此外，HMR还允许进行交叉证据合成，将随机对照试验的汇总结果与单个受试者数据（IPD）相结合，以预测单臂观察性研究的有效性。在本文中，我们评估HMR方法使用模拟数据的例子。我们提出了一个新的糖尿病研究中的真实的案例研究，沿着一个新的R包称为jarbes（只是一个贝叶斯证据合成），它自动化了HMR中涉及的复杂计算。
The hierarchical metaregression (HMR) approach is a multiparameter Bayesian approach for meta-analysis, which generalizes the standard mixed effects models by explicitly modeling the data collection process in the meta-analysis. The HMR allows to investigate the potential external validity of experimental results as well as to assess the internal validity of the studies included in a systematic review. The HMR automatically identifies studies presenting conflicting evidence and it downweights their influence in the meta-analysis. In addition, the HMR allows to perform cross-evidence synthesis, which combines aggregated results from randomized controlled trials to predict effectiveness in a single-arm observational study with individual participant data (IPD). In this paper, we evaluate the HMR approach using simulated data examples. We present a new real case study in diabetes research, along with a new R package called jarbes (just a rather Bayesian evidence synthesis), which automatizes the complex computations involved in the HMR.