A general approach to sensitivity analysis for Mendelian randomization.

Pub Date : 2021-04-01 Epub Date: 2020-04-28 DOI:10.1007/s12561-020-09280-5
Weiming Zhang, Debashis Ghosh
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引用次数: 3

Abstract

Mendelian Randomization (MR) represents a class of instrumental variable methods using genetic variants. It has become popular in epidemiological studies to account for the unmeasured confounders when estimating the effect of exposure on outcome. The success of Mendelian Randomization depends on three critical assumptions, which are difficult to verify. Therefore, sensitivity analysis methods are needed for evaluating results and making plausible conclusions. We propose a general and easy to apply approach to conduct sensitivity analysis for Mendelian Randomization studies. Bound et al. (1995) derived a formula for the asymptotic bias of the instrumental variable estimator. Based on their work, we derive a new sensitivity analysis formula. The parameters in the formula include sensitivity parameters such as the correlation between instruments and unmeasured confounder, the direct effect of instruments on outcome and the strength of instruments. In our simulation studies, we examined our approach in various scenarios using either individual SNPs or unweighted allele score as instruments. By using a previously published dataset from researchers involving a bone mineral density study, we demonstrate that our proposed method is a useful tool for MR studies, and that investigators can combine their domain knowledge with our method to obtain bias-corrected results and make informed conclusions on the scientific plausibility of their findings.

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孟德尔随机化敏感性分析的一般方法。
孟德尔随机化(MR)代表了一类使用遗传变异的工具变量方法。在流行病学研究中,在估计暴露对结果的影响时,考虑未测量的混杂因素已经变得很流行。孟德尔随机化的成功取决于三个难以验证的关键假设。因此,需要灵敏度分析方法来评价结果,得出合理的结论。我们提出了一种通用且易于应用的方法来进行孟德尔随机化研究的敏感性分析。Bound等人(1995)导出了工具变量估计量渐近偏差的公式。在此基础上,推导出一个新的灵敏度分析公式。公式中的参数包括灵敏度参数,如仪器与未测混杂因素之间的相关性、仪器对结果的直接影响以及仪器的强度。在我们的模拟研究中,我们使用单个snp或未加权等位基因评分作为工具,在各种情况下检验了我们的方法。通过使用先前发表的涉及骨矿物质密度研究的研究人员的数据集,我们证明了我们提出的方法是MR研究的有用工具,并且研究人员可以将他们的领域知识与我们的方法相结合,以获得偏差纠正的结果,并就其发现的科学合理性做出明智的结论。
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