Using Instruments for Selection to Adjust for Selection Bias in Mendelian Randomization

TL;DR

This paper reviews and applies instrumental variable methods, including Heckman's approach, to adjust for selection bias in Mendelian randomization studies with missing data, demonstrating their effectiveness through simulations and real data analysis.

Contribution

It introduces how to adapt Heckman's selection model and related methods for Mendelian randomization with missing data, including using genetic variants as instruments for selection bias correction.

Findings

Both methods can mitigate selection bias in Mendelian randomization.

Methods may produce large standard errors in some settings.

Application to real data illustrates practical utility.

Abstract

Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing not at random, Heckman's sample selection model can be used to adjust for bias due to missing data. In this paper, we review Heckman's method and a similar approach proposed by Tchetgen Tchetgen and Wirth (2017). We then discuss how to apply these methods to Mendelian randomization analyses using individual-level data, with missing data for either the exposure or outcome or both. We explore whether genetic variants associated with participation can be used as instruments for selection. We then describe how to obtain missingness-adjusted Wald ratio, two-stage least squares and inverse variance weighted estimates. The two methods are evaluated and compared in simulations, with results suggesting that they can both mitigate selection bias but may yield parameter estimates with large standard errors in some settings. In an illustrative real-data application, we investigate the effects of body mass index on smoking using data from the Avon Longitudinal Study of Parents and Children.