🤖 AI Summary
This study addresses the severe inflation of Type I error that arises in conventional Mendelian randomization (MR) analyses when exposure and outcome variables are unknown and a preliminary association screening is performed prior to MR. To overcome this issue, the authors propose, for the first time, an MR testing framework robust to such pre-screening procedures. They introduce a novel causal test statistic with screening invariance, which integrates external summary statistics to rigorously control the Type I error rate under the null hypothesis of no causal effect while substantially improving statistical power. Extensive simulations demonstrate that the proposed method consistently outperforms standard MR approaches across diverse scenarios, offering both enhanced reliability and efficiency.
📝 Abstract
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.