Coordination Center for Clinical Trials, Düsseldorf University Hospital, Düsseldorf, Germany.
Biom J. 2021 Feb;63(2):406-422. doi: 10.1002/bimj.201900376. Epub 2020 Sep 30.
Public health researchers may have to decide whether to perform a meta-analysis including only high-quality randomized clinical trials (RCTs) or whether to include a mixture of all the available evidence, namely RCTs of varying quality and observational studies (OS). The main hurdle when combining disparate evidence in a meta-analysis is that we are not only combining results of interest but we are also combining multiple biases. Therefore, commonly applied meta-analysis methods may lead to misleading conclusions. In this paper, we present a new Bayesian hierarchical model, called the bias-corrected (BC) meta-analysis model, to combine different study types in meta-analysis. This model is based on a mixture of two random effects distributions, where the first component corresponds to the model of interest and the second component to the hidden bias structure. In this way, the resulting model of interest is adjusted by the internal validity bias of the studies included in a systematic review. We illustrate the BC model with two meta-analyses: The first one combines RCTs and OS to assess effectiveness of vaccination to prevent invasive pneumococcal disease. The second one investigates the effectiveness of stem cell treatment in heart disease patients. Our results show that ignoring internal validity bias in a meta-analysis may lead to misleading conclusions. However, if a meta-analysis model contemplates a bias adjustment, then RCTs results may increase their precision by including OS in the analysis. The BC model has been implemented in JAGS and R, which facilitate its application in practice.
公共卫生研究人员可能必须决定是进行仅包括高质量随机临床试验（RCT）的荟萃分析，还是纳入所有可用证据，即不同质量的 RCT 和观察性研究（OS）的混合。在荟萃分析中组合不同证据的主要障碍是，我们不仅要组合感兴趣的结果，还要组合多种偏倚。因此，通常应用的荟萃分析方法可能会导致误导性的结论。在本文中，我们提出了一种新的贝叶斯层次模型，称为校正偏倚（BC）荟萃分析模型，用于在荟萃分析中组合不同的研究类型。该模型基于两个随机效应分布的混合，其中第一个分量对应于感兴趣的模型，第二个分量对应于隐藏的偏倚结构。通过这种方式，系统评价中包含的研究的内部有效性偏倚对感兴趣的模型进行调整。我们通过两个荟萃分析来说明 BC 模型：第一个结合 RCT 和 OS 来评估疫苗接种预防侵袭性肺炎球菌病的有效性。第二个调查干细胞治疗在心脏病患者中的效果。我们的结果表明，荟萃分析中忽略内部有效性偏倚可能会导致误导性的结论。但是，如果荟萃分析模型考虑了偏差调整，那么将 OS 纳入分析可能会增加 RCT 结果的精度。BC 模型已在 JAGS 和 R 中实现，这便于其在实践中的应用。
