The effect of non-differential measurement error on bias, precision and power in Mendelian randomization studies

Mendelian randomization (MR) studies assess the causality of associations between exposures and disease outcomes using data on genetic determinants of the exposure. In this work, we explore the effect of exposure and outcome measurement error in MR studies.

For continuous traits, we describe measurement error in terms of a theoretical regression of the measured variable on the true variable. We quantify error in terms of the slope (calibration) and the R(2) values (discrimination or classical measurement error). We simulated cohort data sets under realistic parameters and used two-stage least squares regression to assess the effect of measurement error for continuous exposures and outcomes on bias, precision and power. For simulations of binary outcomes, we varied sensitivity and specificity.

Discrimination error in continuous exposures and outcomes did not bias the MR estimate, and only outcome discrimination error substantially reduced power. Calibration error biased the MR estimate when the exposure and the outcome measures were not calibrated in a similar fashion, but power was not affected. For binary outcomes, exposure calibration error introduced substantial bias (with negligible impact on power), but exposure discrimination error did not. Reduced outcome specificity and, to a lesser degree, reduced sensitivity biased MR estimates towards the null.

Understanding the potential effects of measurement error is an important consideration when interpreting estimates from MR analyses. Based on these results, future MR studies should consider methods for accounting for such error and minimizing its impact on inferences derived from MR analyses.