• Matthew Sperrin, Jane Candlish, Ellena Badrick, Andrew Renehan, and Iain Buchan.
• From the aHealth eResearch Centre, Farr Institute, and bInstitute of Cancer Sciences, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.
• Epidemiology. 2016 Jul 1; 27 (4): 525-30.

Background"Obesity paradox" refers to an association between obesity and reduced mortality (contrary to an expected increased mortality). A common explanation is collider stratification bias: unmeasured confounding induced by selection bias. Here, we test this supposition through a realistic generative model.MethodsWe quantify the collider stratification bias in a selected population using counterfactual causal analysis. We illustrate the bias for a range of scenarios, describing associations between exposure (obesity), outcome (mortality), mediator (in this example, diabetes) and an unmeasured confounder.ResultsCollider stratification leads to biased estimation of the causal effect of exposure on outcome. However, the bias is small relative to the causal relationships between the variables.ConclusionsCollider bias can be a partial explanation of the obesity paradox, but unlikely to be the main explanation for a reverse direction of an association to a true causal relationship. Alternative explanations of the obesity paradox should be explored. See Video Abstract at http://links.lww.com/EDE/B51.

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