论文标题
由于测量误差的暴露，多变量的孟德尔随机研究偏见
Bias in multivariable Mendelian randomization studies due to measurement error on exposures
论文作者
论文摘要
多变量孟德尔随机化估计多个暴露对结果的因果影响，通常使用遗传变异关联的摘要统计数据。但是，孟德尔随机应用中感兴趣的暴露通常会以错误来衡量。因此，摘要统计数据将不是与暴露的遗传关联，而是通过误差衡量的暴露。经典测量误差不会偏向遗传关联估计，而是会增加其标准误差。通过一次暴露，这将导致两个样本框架中的偏差。但是，多个相关的暴露不一定是这种情况。在本文中，我们研究了多变量孟德尔随机研究中偏差的方向和大小以及覆盖率，功率和I型错误率如何受到暴露的测量误差的影响。我们展示了如何在最大似然框架中考虑测量误差。我们考虑两个应用的例子。首先，我们表明测量误差会导致体重指数对高估的冠心病风险的影响，而腰围比的影响被低估了。在第二个中，我们表明教育对冠心病风险的影响的比例是由体重指数介导的，如果不考虑测量误差，可能会低估吸烟和血压。
Multivariable Mendelian randomization estimates the causal effect of multiple exposures on an outcome, typically using summary statistics of genetic variant associations. However, exposures of interest in Mendelian randomization applications will often be measured with error. The summary statistics will therefore not be of the genetic associations with the exposure, but with the exposure measured with error. Classical measurement error will not bias genetic association estimates but will increase their standard errors. With a single exposure, this will result in bias toward the null in a two sample framework. However, this will not necessarily be the case with multiple correlated exposures. In this paper, we examine how the direction and size of bias, as well as coverage, power and type I error rates in multivariable Mendelian randomization studies are affected by measurement error on exposures. We show how measurement error can be accounted for in a maximum likelihood framework. We consider two applied examples. In the first, we show that measurement error leads to the effect of body mass index on coronary heart disease risk to be overestimated, and that of waist-to-hip ratio to be underestimated. In the second, we show that the proportion of the effect of education on coronary heart disease risk which is mediated by body mass index, smoking and blood pressure may be underestimated if measurement error is not taken into account.
