与许多无效的遗传仪器乘以坚硬门德尔兰随机化(MR$__2美元) (On Multiply Robust Mendelian Randomization (MR$^2$) With Many Invalid Genetic Instruments)

Mendelian randomization (MR) is a popular instrumental variable (IV) approach, in which genetic markers are used as IVs. In order to improve efficiency, multiple markers are routinely used in MR analyses, leading to concerns about bias due to possible violation of IV exclusion restriction of no direct effect of any IV on the outcome other than through the exposure in view. To address this concern, we introduce a new class of Multiply Robust MR (MR$^2$) estimators that are guaranteed to remain consistent for the causal effect of interest provided that at least one genetic marker is a valid IV without necessarily knowing which IVs are invalid. We show that the proposed MR$^2$ estimators are a special case of a more general class of estimators that remain consistent provided that a set of at least $k^{\dag}$ out of $K$ candidate instrumental variables are valid, for $k^{\dag}\leq K$ set by the analyst ex ante, without necessarily knowing which IVs are invalid. We provide formal semiparametric theory supporting our results, and characterize the semiparametric efficiency bound for the exposure causal effect which cannot be improved upon by any regular estimator with our favorable robustness property. We conduct extensive simulation studies and apply our methods to a large-scale analysis of UK Biobank data, demonstrating the superior empirical performance of MR$^2$ compared to competing MR methods.

翻译：为了提高效率,在管理分析中经常使用多种标记,从而引起对可能违反四类排除限制的偏见的关切,任何四类排除限制除接触外,对结果不会产生直接的影响。为了解决这一关切,我们引入了一个新的类别,即Muliply Robust MR(MR$=2美元),保证对利息的因果关系保持一致,条件是至少一个基因标记是有效的四类,不一定知道什么是无效的。我们表明,拟议的MR$2的估算值是一个特殊的例子,因为可能违反四类排除限制,而任何四类排除对结果没有直接影响,除了通过接触外,对结果没有直接影响。为了解决这一关切,我们引入了一个新的类别,即MMMM(MR)(M)(MR)(M) (M) (MR) (MR) (M) (MR) (MR) (M) (MMR) (M) (M) (MR) (MM) (MR$=2) (M) (M) (MR) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M(M) (M(M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M)