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.

中文翻译：

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用于组合不同类型和质量研究的偏倚校正荟萃分析模型

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，还是包括所有可用证据的混合，即不同质量的随机对照试验和观察性研究 (OS)。在荟萃分析中结合不同证据的主要障碍是，我们不仅结合了感兴趣的结果，而且还结合了多种偏见。因此，常用的荟萃分析方法可能会导致误导性结论。在本文中，我们提出了一种新的贝叶斯层次模型，称为偏差校正 (BC) 元分析模型，以在元分析中结合不同的研究类型。该模型基于两种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置结构。通过这种方式，产生的感兴趣模型会根据系统评价中包含的研究的内部有效性偏差进行调整。我们用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疾病的有效性。第二个研究了干细胞治疗对心脏病患者的有效性。我们的结果表明，在荟萃分析中忽略内部有效性偏差可能会导致误导性结论。但是，如果荟萃分析模型考虑了偏差调整，则 RCT 结果可能会通过在分析中包含 OS 来提高其精度。BC 模型已在 JAGS 和 R 中实现，