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.

中文翻译：

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两样本汇总数据孟德尔随机化中的弱仪器稳健检验

孟德尔随机化 (MR) 一直是遗传流行病学中的一种流行方法，用于使用遗传变异作为工具变量 (IV) 来估计暴露对结果的影响，其中双样本汇总数据 MR 最为流行。不幸的是，MR 研究中的仪器通常与暴露相关性较弱，这可能会影响效应估计并增加 I 类错误。在这项工作中，我们通过将 Anderson–Rubin、Kleibergen 和计量经济学中的条件似然比检验扩展到双样本汇总数据 MR，提出了在弱工具渐近下稳健的检验统计量。我们还使用建议的 Anderson–Rubin 测试来开发点估计器并检测无效工具。