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Classical epidemiology has focused on the control of confounding but it isonly recently that epidemiologists have started to focus on the bias producedby colliders. A collider for a certain pair of variables (e.g., an outcome Yand an exposure A) is a third variable (C) that is caused by both. In adirected acyclic graph (DAG), a collider is the variable in the middle of aninverted fork (i.e., the variable C in A -> C <- Y). Controlling for, orconditioning an analysis on a collider (i.e., through stratification orregression) can introduce a spurious association between its causes. Thispotentially explains many paradoxical findings in the medical literature, whereestablished risk factors for a particular outcome appear protective. We use anexample from non-communicable disease epidemiology to contextualize and explainthe effect of conditioning on a collider. We generate a dataset with 1,000observations and run Monte-Carlo simulations to estimate the effect of 24-hourdietary sodium intake on systolic blood pressure, controlling for age, whichacts as a confounder, and 24-hour urinary protein excretion, which acts as acollider. We illustrate how adding a collider to a regression model introducesbias. Thus, to prevent paradoxical associations, epidemiologists estimatingcausal effects should be wary of conditioning on colliders. We provide R-codein easy-to-read boxes throughout the manuscript and a GitHub repository(https://github.com/migariane/ColliderApp) for the reader to reproduce ourexample. We also provide an educational web application allowing real-timeinteraction to visualize the paradoxical effect of conditioning on a colliderhttp://watzilei.com/shiny/collider/.

international journal of epidemiology

Educational Note: Paradoxical Collider Effect in the Analysis of  Non-Communicable Disease Epidemiological Data: a reproducible illustration  and web application