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

• 成果类型: Article
• 署名作者: Ye, Ting; Liu, Zhonghua; Sun, Baoluo; Tchetgen, Eric Tchetgen
• 署名单位: University of Washington; University of Washington Seattle; Columbia University; National University of Singapore; University of Pennsylvania; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkae024
• 发表日期: 2024
• 页码: 1045-1067
• 关键词: body-mass index; generalized-method; Empirical Likelihood; asymptotic efficiency; CONSISTENT ESTIMATION; variables estimation; genetic-variants; identification; pleiotropy; moments

摘要

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.