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孟德尔随机化敏感性分析的一般方法。

A general approach to sensitivity analysis for Mendelian randomization.

作者信息

Zhang Weiming, Ghosh Debashis

机构信息

Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, U.S.A.

出版信息

Stat Biosci. 2021 Apr;13(1):34-55. doi: 10.1007/s12561-020-09280-5. Epub 2020 Apr 28.

DOI:10.1007/s12561-020-09280-5
PMID:33737984
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7962901/
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.

摘要

孟德尔随机化(MR)是一类使用基因变异的工具变量方法。在流行病学研究中,当估计暴露对结局的影响时,它已被广泛应用于处理未测量的混杂因素。孟德尔随机化的成功取决于三个关键假设,而这些假设很难验证。因此,需要敏感性分析方法来评估结果并得出合理的结论。我们提出了一种通用且易于应用的方法,用于对孟德尔随机化研究进行敏感性分析。Bound等人(1995年)推导了工具变量估计量的渐近偏差公式。基于他们的工作,我们推导了一个新的敏感性分析公式。该公式中的参数包括敏感性参数,如工具变量与未测量混杂因素之间的相关性、工具变量对结局的直接效应以及工具变量的强度。在我们的模拟研究中,我们使用单个单核苷酸多态性(SNP)或未加权等位基因分数作为工具变量,在各种场景下检验了我们的方法。通过使用研究人员先前发表的一个涉及骨密度研究的数据集,我们证明了我们提出的方法是孟德尔随机化研究的一个有用工具,研究人员可以将他们的领域知识与我们的方法相结合,以获得偏差校正后的结果,并就其研究结果的科学合理性得出明智的结论。

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On the use of kernel machines for Mendelian randomization.关于核机器在孟德尔随机化中的应用。
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