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

04/16/2018
by   Stephen Burgess, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/18/2020

Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach

The use of genetic variants as instrumental variables - an approach know...
research
12/21/2020

How to estimate the association between change in a risk factor and a health outcome?

Estimating the effect of a change in a particular risk factor and a chro...
research
09/07/2018

A Primer on Causality in Data Science

Many questions in Data Science are fundamentally causal in that our obje...
research
08/14/2018

A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models

We consider the problem in precision health of grouping people into subp...
research
05/16/2018

Magnitude of selection bias in road safety epidemiology, a primer

In the field of road safety epidemiology, it is common to use responsibi...

Please sign up or login with your details

Forgot password? Click here to reset