SELECTING INVALID INSTRUMENTS TO IMPROVE MENDELIAN RANDOMIZATION WITH TWO-SAMPLE SUMMARY DATA

• 成果类型: Article
• 署名作者: Patel, Ashish; Ditraglia, Francis J.; Zuber, Verena; Burgess, Stephen
• 署名单位: MRC Biostatistics Unit; University of Cambridge; University of Oxford; Imperial College London
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1856
• 发表日期: 2024
• 页码: 1729-1749
• 关键词: inference

摘要

Mendelian randomization (MR) is a widely-used method to estimate the causal relationship between a risk factor and disease. A fundamental part of any MR analysis is to choose appropriate genetic variants as instrumental variables. Genome-wide association studies often reveal that hundreds of genetic variants may be robustly associated with a risk factor, but in some situations investigators may have greater confidence in the instrument validity of only a smaller subset of variants. Nevertheless, the use of additional instruments may be optimal from the perspective of mean squared error, even if they are slightly invalid; a small bias in estimation may be a price worth paying for a larger reduction in variance. For this purpose we consider a method for focused instrument selection whereby genetic variants are selected to minimise the estimated asymptotic mean squared error of causal effect estimates. In a setting of many weak and locally invalid instruments, we propose a novel strategy to construct confidence intervals for postselection focused estimators that guards against the worst case loss in asymptotic coverage. In empirical applications to: (i) validate lipid drug targets and (ii) investigate vitamin D effects on a wide range of outcomes, our findings suggest that the optimal selection of instruments does not involve only a small number of biologically-justified instruments but also many potentially invalid instruments.