PDF(Pubmed)
Abstract：
Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy.
摘要：
观察数据提供了医学中宝贵的现实世界信息，但是需要某些方法论上的考虑来得出因果估计。在这次系统审查中，我们评估了使用非随机暴露进行的个体水平患者数据荟萃分析（IPD-MA）的方法和报告质量，发表于2009年、2014年和2019年，试图估计医学中的因果关系。我们筛选了超过16,000个标题和摘要，在167篇被认为可能符合条件的文章中，审查了45篇全文，并将29项纳入分析。不幸的是,我们发现因果方法很少被实施，和报告一般较差的研究。具体来说,29篇文章中只有3篇使用了准实验方法，没有研究使用G方法来调整时变混杂因素。为了解决这些问题,我们建议医生和方法学家之间加强合作，以确保因果方法在IPD-MA中得到正确实施。此外,我们提出了使用因果方法的IPD-MA报告指南的建议清单。该清单可以改善报告，从而潜在地提高IPD-MA的质量和可信度，这可以被认为是卫生政策最有价值的证据来源之一。
