Bias in multivariable Mendelian randomization studies due to measurement error on exposures
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暴露测量误差导致的多变量孟德尔随机化偏倚
- 研究问题
- 多变量孟德尔随机化中，相关暴露的测量误差如何改变因果效应估计的偏倚方向与大小，并影响覆盖率、功效和第一类错误率？
- 方法与思路
- 作者分析暴露测量误差对遗传关联汇总统计及多变量推断的影响，提出最大似然处理框架，并以冠心病相关的两个应用说明。
- 材料陈述的贡献
- 摘要称，多暴露情形的偏倚未必趋向零；应用显示体重指数效应可能高估，腰臀比效应及教育效应的部分中介比例可能低估。
- 阅读关注点
- 应核对测量误差及暴露相关结构的设定、最大似然所需信息、弱工具变量的处理，以及应用中偏倚方向判断的依据。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及期刊接收未经核实；摘要未说明误差校正的完整条件，应用中的偏倚方向不能直接推广。
原文摘要
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.