AI 中文总结
研究旨在解决孟德尔随机化中先筛选关联再评估因果效应导致的I型错误膨胀问题，提出新颖稳健MR检验，该检验能保持正确I型错误率且可利用先前汇总统计量提高功效，模拟研究显示其优于经典方法。
AI 中文摘要
孟德尔随机化（MR）在生物医学研究中被广泛用作识别因果效应的标准方法。当众多潜在候选变量中真实的暴露和结果变量未知时，常见但有问题的做法是先筛选关联，然后仅在显示显著相关性的暴露 - 结果对中评估因果效应。我们证明经典MR比率估计量在此选择过程下存在严重的I型错误膨胀。为解决此问题，我们提出一种新颖的稳健MR检验，当不存在真实因果效应时，无论先前的关联筛选步骤如何都保持有效。我们表明所提出的检验始终保持正确的I型错误率，与关联检验结果无关。此外，我们的方法可以纳入先前关联研究的汇总统计量以提高因果效应检测的功效。大量模拟研究说明了所提出方法与经典MR方法相比的优势。
英文摘要
Mendelian Randomization (MR) has been widely used as a standard approach for identifying causal effects in biomedical research. When the true exposure and outcome variables are unknown among many potential candidates, a common but problematic practice is to first screen for associations and then evaluate causal effects only among exposure-outcome pairs that show significant correlations. We demonstrate that the classical MR ratio estimator suffers from severe type I error inflation under this selection procedure. To address this issue, we propose a novel robust MR test that remains valid regardless of the prior association screening step when there is no true causal effect. We show that the proposed test consistently maintains the correct type I error rate, independent of the association test results. Furthermore, our method can incorporate summary statistics from previous association studies to improve the power of causal effect detection. Extensive simulation studies illustrate the advantages of the proposed method compared with the classical MR approach.
