GENIUS-MAWII: for robust Mendelian randomization with many weak invalid instruments.

IF 3.1 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series B-Statistical Methodology Pub Date : 2024-03-14 eCollection Date: 2024-09-01 DOI:10.1093/jrsssb/qkae024

Ting Ye, Zhonghua Liu, Baoluo Sun, Eric Tchetgen Tchetgen

引用次数: 0

Abstract

Mendelian randomization (MR) addresses causal questions using genetic variants as instrumental variables. We propose a new MR method, G-Estimation under No Interaction with Unmeasured Selection (GENIUS)-MAny Weak Invalid IV, which simultaneously addresses the 2 salient challenges in MR: many weak instruments and widespread horizontal pleiotropy. Similar to MR-GENIUS, we use heteroscedasticity of the exposure to identify the treatment effect. We derive influence functions of the treatment effect, and then we construct a continuous updating estimator and establish its asymptotic properties under a many weak invalid instruments asymptotic regime by developing novel semiparametric theory. We also provide a measure of weak identification, an overidentification test, and a graphical diagnostic tool.

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GENIUS-MAWII：用于有许多弱无效工具的稳健孟德尔随机化。

孟德尔随机化（Mendelian randomization，MR）利用遗传变异作为工具变量来解决因果问题。我们提出了一种新的孟德尔随机化方法，即 "未测量选择无交互作用下的 G-估计（GENIUS）-MAny Weak Invalid IV"，它同时解决了孟德尔随机化的两大难题：许多弱工具和广泛的水平多义性。与 MR-GENIUS 类似，我们利用暴露的异方差性来识别治疗效果。我们推导出了治疗效果的影响函数，然后构建了一个连续更新估计器，并通过发展新颖的半参数理论，确立了它在许多弱无效工具渐近机制下的渐近特性。我们还提供了弱识别度量、过度识别检验和图形诊断工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Royal Statistical Society Series B-Statistical Methodology 数学-统计学与概率论

CiteScore

8.80

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science. Reviews of methodological techniques are also considered. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains or if it is dominated by computations or simulations of a routine nature.