Contextualizing selection bias in Mendelian randomization: how bad is it likely to be?
Int J Epidemiol
Oxford University Press (OUP)
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Gkatzionis, A., & Burgess, S. (2019). Contextualizing selection bias in Mendelian randomization: how bad is it likely to be?. Int J Epidemiol, 48 (3), 691-701. https://doi.org/10.1093/ije/dyy202
BACKGROUND: 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 with other potential biases is unclear. METHODS: We performed an extensive simulation study to assess the impact of selection bias on a typical Mendelian randomization investigation. We considered inverse probability weighting as a potential method for reducing selection bias. 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. RESULTS: 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. 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. In the example of lipoprotein(a), strong genetic associations and strong confounder effects on selection mean the reported null effect on cardiovascular mortality could plausibly be explained by selection bias. CONCLUSIONS: Selection bias can adversely affect Mendelian randomization investigations, but its impact is likely to be less than other biases. Selection bias is substantial when the effects of the risk factor and confounders on selection are particularly large.
causal inference, collider bias, instrumental variables, inverse probability weighting, selection bias, Cardiovascular Diseases, Causality, Computer Simulation, Confounding Factors, Epidemiologic, Coronary Disease, Humans, Lipoprotein(a), Mendelian Randomization Analysis, Risk Factors, Selection Bias
This work was supported by the UK Medical Research Council (Core Medical Research Council Biostatistics Unit Funding Code: MC UU 00002/7). Apostolos Gkatzionis was supported by a Medical Research Council Methodology Research Panel grant (Grant Number RG88311). Stephen Burgess was supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 204623/Z/16/Z).
Wellcome Trust (204623/Z/16/Z)
Medical Research Council (MR/L003120/1)
Medical Research Council (MR/N027493/1)
British Heart Foundation (None)
Medical Research Council (MC_UU_00002/7)
External DOI: https://doi.org/10.1093/ije/dyy202
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285327
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/
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