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基于电子数据库的孟德尔随机化研究与随机对照试验的系统比较。

Systematic comparison of Mendelian randomisation studies and randomised controlled trials using electronic databases.

机构信息

MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK

MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK.

出版信息

BMJ Open. 2023 Sep 26;13(9):e072087. doi: 10.1136/bmjopen-2023-072087.

Abstract

OBJECTIVE

To scope the potential for (semi)-automated triangulation of Mendelian randomisation (MR) and randomised controlled trials (RCTs) evidence since the two methods have distinct assumptions that make comparisons between their results invaluable.

METHODS

We mined ClinicalTrials.Gov, PubMed and EpigraphDB databases and carried out a series of 26 manual literature comparisons among 54 MR and 77 RCT publications.

RESULTS

We found that only 13% of completed RCTs identified in ClinicalTrials.Gov submitted their results to the database. Similarly low coverage was revealed for Semantic Medline (SemMedDB) semantic triples derived from MR and RCT publications -36% and 12%, respectively. Among intervention types that can be mimicked by MR, only trials of pharmaceutical interventions could be automatically matched to MR results due to insufficient annotation with Medical Subject Headings ontology. A manual survey of the literature highlighted the potential for triangulation across a number of exposure/outcome pairs if these challenges can be addressed.

CONCLUSIONS

We conclude that careful triangulation of MR with RCT evidence should involve consideration of similarity of phenotypes across study designs, intervention intensity and duration, study population demography and health status, comparator group, intervention goal and quality of evidence.

摘要

目的

探讨孟德尔随机化(MR)和随机对照试验(RCT)证据半自动三角剖分的可能性,因为这两种方法具有不同的假设,使得它们的结果之间的比较具有重要价值。

方法

我们挖掘了 ClinicalTrials.Gov、PubMed 和 EpigraphDB 数据库,并对 54 篇 MR 和 77 篇 RCT 文献进行了一系列 26 次手动文献比较。

结果

我们发现,ClinicalTrials.Gov 中仅 13%的已完成 RCT 向数据库提交了结果。从 MR 和 RCT 出版物中衍生的 Semantic Medline(SemMedDB)语义三元组的覆盖范围也很低,分别为 36%和 12%。在可以通过 MR 模拟的干预类型中,由于缺乏对医学主题词本体的注释,只有药物干预的试验可以与 MR 结果自动匹配。对文献的手动调查突出了如果能够解决这些挑战,许多暴露/结局对之间的三角剖分的潜力。

结论

我们的结论是,MR 与 RCT 证据的仔细三角剖分应考虑研究设计、干预强度和持续时间、研究人群人口统计学和健康状况、对照组、干预目标和证据质量等方面的表型相似性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/671c/10533809/80dd71ee4c41/bmjopen-2023-072087f01.jpg

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