Unbiased estimation and asymptotically valid inference in multivariable Mendelian randomization with many weak instrumental variables
多弱工具变量下多变量孟德尔随机化的无偏估计与有效渐近推断AI 解读 ↓
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多弱工具变量下多变量孟德尔随机化的无偏估计与有效渐近推断
- 研究问题
- 多变量孟德尔随机化同时存在大量弱工具变量、水平多效性和 GWAS 样本重叠时，如何解释估计偏差并改进因果推断？
- 方法与思路
- 作者研究逆方差加权估计的变量测量误差偏差，建立其与偏差校正估计方程方法的渐近理论，并用于近视相关暴露分析。
- 材料陈述的贡献
- 摘要称，弱工具影响偏差尺度，样本重叠影响偏差方向；所提方法可兼顾水平多效性，并改善无偏性和渐近推断有效性。
- 阅读关注点
- 应核对工具有效性与多效性处理条件、弱工具及样本重叠的渐近设定，以及无偏性结论和近视应用因果解释的边界。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未给出完整工具变量假设，不能据此确认应用中的因果结论。
原文摘要
Mendelian randomization (MR) is an instrumental variable (IV) approach to infer causal relationships between exposures and outcomes with genome-wide association studies (GWAS) summary data. However, the multivariable inverse-variance weighting (IVW) approach, which serves as the foundation for most MR approaches, cannot yield unbiased causal effect estimates in the presence of many weak IVs. To address this problem, we proposed the MR using Bias-corrected Estimating Equation (MRBEE) that can infer unbiased causal relationships with many weak IVs and account for horizontal pleiotropy simultaneously. While the practical significance of MRBEE was demonstrated in our parallel work (Lorincz-Comi (2023)), this paper established the statistical theories of multivariable IVW and MRBEE with many weak IVs. First, we showed that the bias of the multivariable IVW estimate is caused by the error-in-variable bias, whose scale and direction are inflated and influenced by weak instrument bias and sample overlaps of exposures and outcome GWAS cohorts, respectively. Second, we investigated the asymptotic properties of multivariable IVW and MRBEE, showing that MRBEE outperforms multivariable IVW regarding unbiasedness of causal effect estimation and asymptotic validity of causal inference. Finally, we applied MRBEE to examine myopia and revealed that education and outdoor activity are causal to myopia whereas indoor activity is not.