Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Int J Epidemiol. 2023 Feb 8;52(1):227-249. doi: 10.1093/ije/dyac149.
The use of Mendelian randomization (MR) in epidemiology has increased considerably in recent years, with a subsequent increase in systematic reviews of MR studies. We conducted a systematic review of tools designed for assessing risk of bias and/or quality of evidence in MR studies and a review of systematic reviews of MR studies.
We systematically searched MEDLINE, Embase, the Web of Science, preprints servers and Google Scholar for articles containing tools for assessing, conducting and/or reporting MR studies. We also searched for systematic reviews and protocols of systematic reviews of MR studies. From eligible articles we collected data on tool characteristics and content, as well as details of narrative description of bias assessment.
Our searches retrieved 2464 records to screen, from which 14 tools, 35 systematic reviews and 38 protocols were included in our review. Seven tools were designed for assessing risk of bias/quality of evidence in MR studies and evaluation of their content revealed that all seven tools addressed the three core assumptions of instrumental variable analysis, violation of which can potentially introduce bias in MR analysis estimates.
We present an overview of tools and methods to assess risk of bias/quality of evidence in MR analysis. Issues commonly addressed relate to the three standard assumptions of instrumental variables analyses, the choice of genetic instrument(s) and features of the population(s) from which the data are collected (particularly in two-sample MR), in addition to more traditional non-MR-specific epidemiological biases. The identified tools should be tested and validated for general use before recommendations can be made on their widespread use. Our findings should raise awareness about the importance of bias related to MR analysis and provide information that is useful for assessment of MR studies in the context of systematic reviews.
近年来，孟德尔随机化（MR）在流行病学中的应用显著增加，随之而来的是对 MR 研究的系统评价也有所增加。我们对用于评估 MR 研究偏倚风险和/或证据质量的工具进行了系统评价，并对 MR 研究的系统评价进行了综述。
我们系统地检索了 MEDLINE、Embase、Web of Science、预印本服务器和 Google Scholar，以查找包含用于评估、进行和/或报告 MR 研究的工具的文章。我们还检索了 MR 研究的系统评价和系统评价方案。从合格的文章中，我们收集了工具特征和内容的数据，以及偏倚评估的叙述描述的详细信息。
我们的搜索共检索到 2464 条记录进行筛选，其中包括 14 种工具、35 项系统评价和 38 项方案纳入我们的综述。有 7 种工具用于评估 MR 研究的偏倚风险/证据质量，对其内容的评估表明，这 7 种工具都涉及工具变量分析的三个核心假设，违反这些假设可能会导致 MR 分析估计值产生偏倚。
我们介绍了用于评估 MR 分析中的偏倚风险/证据质量的工具和方法概述。常见的问题涉及工具变量分析的三个标准假设、遗传工具的选择以及数据来源人群的特征（特别是在两样本 MR 中），以及更传统的非 MR 特定的流行病学偏倚。在提出广泛使用的建议之前，应先对这些工具进行测试和验证，以确保其可以普遍使用。我们的研究结果应该引起人们对与 MR 分析相关的偏倚的重视，并提供有关系统评价中评估 MR 研究的有用信息。
