---
title: Contextualizing selection bias in Mendelian randomization: how bad is it
  likely to be?
description:   Selection bias affects Mendelian randomization investigations when selection
into the study sample depends on a collider between the genetic variant and
confounders of the risk factor-outcome association. However, the relative
importance of selection bias for Mendelian randomization compared to other
potential biases is unclear. We performed an extensive simulation study to
assess the impact of selection bias on a typical Mendelian randomization
investigation. Selection bias had a severe impact on bias and Type 1 error
rates in our simulation study, but only when selection effects were large. For
moderate effects of the risk factor on selection, bias was generally small and
Type 1 error rate inflation was not considerable. The magnitude of bias was
also affected by the strength of confounder-risk factor and confounder-outcome
associations, the structure of the causal diagram and selection frequency. The
use of inverse probability weighting ameliorated bias when the selection model
was correctly specified, but increased bias when selection bias was moderate
and the model was misspecified. Finally, we investigated whether selection bias
may explain a recently reported finding that lipoprotein(a) is not a causal
risk factor for cardiovascular mortality in individuals with previous coronary
heart disease.

url: https://modelslab.com/ai-news/contextualizing-selection-bias-in-mendelian-randomization-how-bad-is-it-likely-to-be
canonical: https://modelslab.com/ai-news/contextualizing-selection-bias-in-mendelian-randomization-how-bad-is-it-likely-to-be
type: website
component: Content/AINews/Single
generated_at: 2026-01-20T14:32:30.939313Z
---

# Contextualizing selection bias in Mendelian randomization: how bad is it
  likely to be?

>   Selection bias affects Mendelian randomization investigations when selection
into the study sample depends on a collider between the genetic variant and
confounders of the risk factor-outcome association. However, the relative
importance of selection bias for Mendelian randomization compared to other
potential biases is unclear. We performed an extensive simulation study to
assess the impact of selection bias on a typical Mendelian randomization
investigation. Selection bias had a severe impact on bias and Type 1 error
rates in our simulation study, but only when selection effects were large. For
moderate effects of the risk factor on selection, bias was generally small and
Type 1 error rate inflation was not considerable. The magnitude of bias was
also affected by the strength of confounder-risk factor and confounder-outcome
associations, the structure of the causal diagram and selection frequency. The
use of inverse probability weighting ameliorated bias when the selection model
was correctly specified, but increased bias when selection bias was moderate
and the model was misspecified. Finally, we investigated whether selection bias
may explain a recently reported finding that lipoprotein(a) is not a causal
risk factor for cardiovascular mortality in individuals with previous coronary
heart disease.



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