Luque-fernandez, MA, Schomaker, M, Redondo-Sanchez, D, Perez, MJS, Vaidya, A and Schnitzer, ME. 2018. Educational Note: Paradoxical Collider Effect in the Analysis of Non-Communicable Disease Epidemiological Data: a reproducible illustration and web application. [Online]. Github. Available from: https://github.com/migariane/ColliderApp
Luque-fernandez, MA, Schomaker, M, Redondo-Sanchez, D, Perez, MJS, Vaidya, A and Schnitzer, ME. Educational Note: Paradoxical Collider Effect in the Analysis of Non-Communicable Disease Epidemiological Data: a reproducible illustration and web application. [Internet] LSHTM Data Compass. Github; 2018. Available from: https://github.com/migariane/ColliderApp
Luque-fernandez, MA, Schomaker, M, Redondo-Sanchez, D, Perez, MJS, Vaidya, A and Schnitzer, ME (2018). Educational Note: Paradoxical Collider Effect in the Analysis of Non-Communicable Disease Epidemiological Data: a reproducible illustration and web application. [Data Collection]. Github. https://github.com/migariane/ColliderApp
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 (Published in a 3rd party system) | 2018 |
Language(s) of written materials | English |
Data Creators | Luque-fernandez, MA, 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 |
Depositor | Gareth Knight |
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Date Deposited | 01 Mar 2019 17:20 |
Last Modified | 01 Mar 2019 17:20 |
Publisher | Github |