GENIUS-MAWII:用于有许多弱无效工具的稳健孟德尔随机化。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-03-14 eCollection Date: 2024-09-01 DOI:10.1093/jrsssb/qkae024
Ting Ye, Zhonghua Liu, Baoluo Sun, Eric Tchetgen Tchetgen
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引用次数: 0

摘要

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

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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CiteScore
7.20
自引率
4.30%
发文量
567
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