Luque-Fernandez, Miguel AngelSchomaker, MichaelRedondo-Sanchez, DanielJose Sanchez Perez, MariaVaidya, AnandSchnitzer, Mireille E2023-01-252023-01-252019http://hdl.handle.net/10668/13323Classical epidemiology has focused on the control of confounding, but it is only recently that epidemiologists have started to focus on the bias produced by colliders. A collider for a certain pair of variables (e.g. an outcome Y and an exposure A) is a third variable (C) that is caused by both. In a directed acyclic graph (DAG), a collider is the variable in the middle of an inverted fork (i.e. the variable C in A → C ← Y). Controlling for, or conditioning an analysis on a collider (i.e. through stratification or regression) can introduce a spurious association between its causes. This potentially explains many paradoxical findings in the medical literature, where established risk factors for a particular outcome appear protective. We use an example from non-communicable disease epidemiology to contextualize and explain the effect of conditioning on a collider. We generate a dataset with 1000 observations, and run Monte-Carlo simulations to estimate the effect of 24-h dietary sodium intake on systolic blood pressure, controlling for age, which acts as a confounder, and 24-h urinary protein excretion, which acts as a collider. We illustrate how adding a collider to a regression model introduces bias. Thus, to prevent paradoxical associations, epidemiologists estimating causal effects should be wary of conditioning on colliders. We provide R code in easy-to-read boxes throughout the manuscript, and a GitHub repository [https://github.com/migariane/ColliderApp] for the reader to reproduce our example. We also provide an educational web application allowing real-time interaction to visualize the paradoxical effect of conditioning on a collider [http://watzilei.com/shiny/collider/].enEpidemiological methodscausalitynon-communicable disease epidemiologyBiasCausalityConfounding Factors, EpidemiologicEpidemiologic MethodsHumansLogistic ModelsNoncommunicable Diseasesresearch article30561628open access10.1093/ije/dyy2751464-3685PMC6469301https://academic.oup.com/ije/article-pdf/48/2/640/31517845/dyy275.pdfhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6469301/pdf