Mendelian randomization (MR) has been a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome using genetic variants as instrumental variables (IV), with two-sample summary-data MR being the most popular. Unfortunately, instruments in MR studies are often weakly associated with the exposure, which can bias effect estimates and inflate Type I errors. In this work, we propose test statistics that are robust under weak instrument asymptotics by extending the Anderson-Rubin, Kleibergen, and the conditional likelihood ratio test in econometrics to two-sample summary-data MR. We also use the proposed Anderson-Rubin test to develop a point estimator and to detect invalid instruments. We conclude with a simulation and an empirical study and show that the proposed tests control size and have better power than existing methods with weak instruments.
翻译:在遗传流行病学中,使用基因变量作为工具变量(IV)来估计接触结果对结果的影响的流行方法是门德尔随机化(MR),使用两个样本的简要数据MR(MR)最为流行,不幸的是,MR研究中的仪器往往与接触联系薄弱,这可能会影响估计结果和造成I型错误的偏差。在这项工作中,我们建议通过将Anderson-Rubin(Kleibergen)和生态计量中的有条件概率比测试扩大到两个样本摘要数据MR(M),来评估接触结果对结果的影响。我们还利用提议的Anderson-Rubin测试来开发点测算器和检测无效仪器。我们通过模拟和实验性研究得出结论,并表明拟议的测试控制尺寸比现有工具的强度要强。