论文标题
多变量孟德尔随机化的多效性鲁棒方法
Pleiotropy robust methods for multivariable Mendelian randomization
论文作者
论文摘要
孟德尔随机化是从观察数据中推断出或其他因果影响的强大工具。然而，遗传变异的性质使得多效性仍然是有效因果效应估计的障碍。在研究单个危险因素对结果的影响时，文献中有许多选择的多效方法。但是，在多变量设置中，即当有多个感兴趣的风险因素时，多效性鲁棒方法很少。在本文中，我们介绍了三种基于单变量环境中常见方法的方法：mvmr-bobust; MVMR-MEDIAN;和mvmr-lasso。我们讨论了每种方法的属性，并与模拟研究中的现有方法相比，检查它们的性能。当多效水平较低时，MVMR射击表现出优于现有的异常鲁棒方法。 MVMR-LASSO在中等至高效率的平均误差方面提供了最佳估计，并且可以在三个样本设置中提供有效的推断。 MVMR-MEDIAN在考虑的所有情况下的估计方面表现良好，并提供有效的推断，最高为中等的多效性。我们在一个应用的示例中演示了这些方法，研究智力，教育和家庭收入对阿尔茨海默氏病风险的影响。
Mendelian randomization is a powerful tool for inferring the presence, or otherwise, of causal effects from observational data. However, the nature of genetic variants is such that pleiotropy remains a barrier to valid causal effect estimation. There are many options in the literature for pleiotropy robust methods when studying the effects of a single risk factor on an outcome. However, there are few pleiotropy robust methods in the multivariable setting, that is, when there are multiple risk factors of interest. In this paper we introduce three methods which build on common approaches in the univariable setting: MVMR-Robust; MVMR-Median; and MVMR-Lasso. We discuss the properties of each of these methods and examine their performance in comparison to existing approaches in a simulation study. MVMR-Robust is shown to outperform existing outlier robust approaches when there are low levels of pleiotropy. MVMR-Lasso provides the best estimation in terms of mean squared error for moderate to high levels of pleiotropy, and can provide valid inference in a three sample setting. MVMR-Median performs well in terms of estimation across all scenarios considered, and provides valid inference up to a moderate level of pleiotropy. We demonstrate the methods in an applied example looking at the effects of intelligence, education and household income on the risk of Alzheimer's disease.
