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证据综合中的因果评估:综述方法的回顾。

Causal assessment in evidence synthesis: A methodological review of reviews.

机构信息

MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK.

出版信息

Res Synth Methods. 2022 Jul;13(4):405-423. doi: 10.1002/jrsm.1569. Epub 2022 Jun 9.

DOI:10.1002/jrsm.1569
PMID:35560730
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9543433/
Abstract

In fields (such as population health) where randomised trials are often lacking, systematic reviews (SRs) can harness diversity in study design, settings and populations to assess the evidence for a putative causal relationship. SRs may incorporate causal assessment approaches (CAAs), sometimes called 'causal reviews', but there is currently no consensus on how these should be conducted. We conducted a methodological review of self-identifying 'causal reviews' within the field of population health to establish: (1) which CAAs are used; (2) differences in how CAAs are implemented; (3) how methods were modified to incorporate causal assessment in SRs. Three databases were searched and two independent reviewers selected reviews for inclusion. Data were extracted using a standardised form and summarised using tabulation and narratively. Fifty-three reviews incorporated CAAs: 46/53 applied Bradford Hill (BH) viewpoints/criteria, with the remainder taking alternative approaches: Medical Research Council guidance on natural experiments (2/53, 3.8%); realist reviews (2/53, 3.8%); horizontal SRs (1/53, 1.9%); 'sign test' of causal mechanisms (1/53, 1.9%); and a causal cascade model (1/53, 1.9%). Though most SRs incorporated BH, there was variation in application and transparency. There was considerable overlap across the CAAs, with a trade-off between breadth (BH viewpoints considered a greater range of causal characteristics) and depth (many alternative CAAs focused on one viewpoint). Improved transparency in the implementation of CAA in SRs in needed to ensure their validity and allow robust assessments of causality within evidence synthesis.

摘要

在随机试验通常缺乏的领域(如人群健康),系统评价(SRs)可以利用研究设计、环境和人群的多样性来评估假定因果关系的证据。SRs 可以纳入因果评估方法(CAAs),有时称为“因果评价”,但目前对于如何进行这些方法还没有共识。我们对人群健康领域中自我识别的“因果评价”进行了方法学回顾,以确定:(1)使用了哪些 CAAs;(2)实施 CAAs 的差异;(3)如何修改方法以将因果评价纳入 SRs。我们检索了三个数据库,并由两名独立审查员选择纳入的评价。使用标准化表格提取数据,并通过制表和叙述性总结进行总结。53 项评价纳入了 CAAs:46/53 项应用了布拉德福德·希尔(BH)观点/标准,其余的则采用了替代方法:医学研究委员会关于自然实验的指南(2/53,3.8%);现实主义评价(2/53,3.8%);水平 SRs(1/53,1.9%);因果机制的“符号检验”(1/53,1.9%);因果级联模型(1/53,1.9%)。尽管大多数 SRs 都纳入了 BH,但在应用和透明度方面存在差异。CAAs 之间存在相当大的重叠,广度(BH 观点考虑了更广泛的因果特征)和深度(许多替代 CAAs 侧重于一个观点)之间存在权衡。需要提高 SR 中 CAA 实施的透明度,以确保其有效性,并允许在证据综合中对因果关系进行稳健评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57fd/9543433/285bd4eb0b55/JRSM-13-405-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57fd/9543433/285bd4eb0b55/JRSM-13-405-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57fd/9543433/285bd4eb0b55/JRSM-13-405-g001.jpg

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