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Credible Mendelian Randomization Studies: Approaches for Evaluating the Instrumental Variable Assumptions

机译:可信的孟德尔随机研究:评估工具变量假设的方法

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摘要

As with other instrumental variable (IV) analyses, Mendelian randomization (MR) studies rest on strong assumptions. These assumptions are not routinely systematically evaluated in MR applications, although such evaluation could add to the credibility of MR analyses. In this article, the authors present several methods that are useful for evaluating the validity of an MR study. They apply these methods to a recent MR study that used fat mass and obesity-associated (FTO) genotype as an IV to estimate the effect of obesity on mental disorder. These approaches to evaluating assumptions for valid IV analyses are not fail-safe, in that there are situations where the approaches might either fail to identify a biased IV or inappropriately suggest that a valid IV is biased. Therefore, the authors describe the assumptions upon which the IV assessments rely. The methods they describe are relevant to any IV analysis, regardless of whether it is based on a genetic IV or other possible sources of exogenous variation. Methods that assess the IV assumptions are generally not conclusive, but routinely applying such methods is nonetheless likely to improve the scientific contributions of MR studies.
机译:与其他工具变量(IV)分析一样,孟德尔随机(MR)研究也基于强大的假设。尽管这些评估可能会增加MR分析的可信度,但是在MR应用程序中不会常规地系统评估这些假设。在本文中,作者提出了几种可用于评估MR研究有效性的方法。他们将这些方法应用于最近的MR研究中,该研究使用脂肪量和肥胖相关(FTO)基因型作为IV来评估肥胖对精神障碍的影响。这些评估有效IV分析的假设的方法不是故障安全的,因为在某些情况下,这些方法可能无法识别有偏差的IV或不适当地暗示有效的IV有偏差。因此,作者描述了IV评估所依据的假设。他们描述的方法与任何IV分析有关,无论它是基于遗传IV还是其他外源变异的可能来源。评估IV假设的方法通常不是结论性的,但是常规应用此类方法仍可能会改善MR研究的科学贡献。

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