Fernandez, MAL, Schomaker, M, Redondo-Sanchez, D, Perez, MJS, Vaidya, A and Schnitzer, ME. 2019. migariane/ColliderApp: ColliderAppLuque. [Online]. Zenodo. Available from: https://doi.org/10.5281/zenodo.2560807
Fernandez, MAL, Schomaker, M, Redondo-Sanchez, D, Perez, MJS, Vaidya, A and Schnitzer, ME. migariane/ColliderApp: ColliderAppLuque [Internet]. Zenodo; 2019. Available from: https://doi.org/10.5281/zenodo.2560807
Fernandez, MAL, Schomaker, M, Redondo-Sanchez, D, Perez, MJS, Vaidya, A and Schnitzer, ME (2019). migariane/ColliderApp: ColliderAppLuque. [Data Collection]. Zenodo. https://doi.org/10.5281/zenodo.2560807
Alternative Title
Educational Note: Paradoxical Collider Effect in the Analysis of Non-Communicable Disease Epidemiological Data: a reproducible illustration and web application
Description
Classical 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 DAGs terminology, 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 used an example from non-communicable disease epidemiology to contextualize and explain the effect of conditioning on a collider. We generated a dataset with 1,000 observations and ran Monte-Carlo simulations to estimate the effect of 24-hour dietary sodium intake on systolic blood pressure, controlling for age, which acts as a confounder, and 24-hour 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
Data capture method | Other |
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Date (Date published in a 3rd party system) | 9 February 2019 |
Language(s) of written materials | English |
Data Creators | Fernandez, MAL, Schomaker, M, Redondo-Sanchez, D, Perez, MJS, Vaidya, A and Schnitzer, ME |
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LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology |
Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom |
Date Deposited | 01 Mar 2019 17:20 |
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Last Modified | 28 Sep 2021 13:14 |
Publisher | Zenodo |