Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates

TL;DR

This paper discusses the interpretation and estimation of causal effects in Mendelian randomization when the exposure is binary, highlighting the assumptions, limitations, and the importance of considering the underlying continuous variable.

Contribution

It provides methods for causal estimation with a binary exposure in Mendelian randomization, emphasizing the role of the underlying continuous risk factor and clarifying assumptions.

Findings

Causal estimates assume a stepwise effect at the dichotomization point.

Estimation requires additional parametric assumptions.

Causal inference is valid for the underlying continuous risk factor.

Abstract

Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions, an association between the variant and outcome implies a causal effect of the exposure on the outcome. Complications arise with a binary exposure that is a dichotomization of a continuous risk factor (for example, hypertension is a dichotomization of blood pressure). This can lead to violation of the exclusion restriction assumption: the genetic variant can influence the outcome via the continuous risk factor even if the binary exposure does not change. Provided the instrumental variable assumptions are satisfied for the underlying continuous risk factor, causal inferences for the binary exposure are valid for the continuous risk factor. Causal estimates for the binary exposure assume the causal effect is a stepwise function at the point of dichotomization. Even then, estimation requires further parametric assumptions. Under monotonicity, the causal estimate represents the average causal effect in `compliers', individuals for whom the binary exposure would be present if they have the genetic variant and absent otherwise. Unlike in randomized trials, genetic compliers are unlikely to be a large or representative subgroup of the population. Under homogeneity, the causal effect of the exposure on the outcome is assumed constant in all individuals; often an unrealistic assumption. We here provide methods for causal estimation with a binary exposure (although subject to all the above caveats). Mendelian randomization investigations with a dichotomized binary exposure should be conceptualized in terms of an underlying continuous variable.