Bias in multivariable Mendelian randomization studies due to measurement error on exposures

TL;DR

This paper investigates how measurement error in exposures affects bias, coverage, and power in multivariable Mendelian randomization studies, proposing a maximum likelihood approach to address these issues.

Contribution

It provides a detailed analysis of measurement error impact on multivariable Mendelian randomization and introduces a maximum likelihood method to correct for bias.

Findings

Measurement error can bias effect estimates in multivariable Mendelian randomization.

The proposed maximum likelihood method can account for measurement error.

Ignoring measurement error may lead to underestimation or overestimation of causal effects.

Abstract

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.