Title
Educational Note: Paradoxical Collider Effect in the Analysis of Non-Communicable Disease Epidemiological Data: a reproducible illustration and web application
Authors
Miguel Angel Luque-Fernandez, Michael Schomaker, Daniel Redondo-Sanchez,
Maria Jose Sanchez Perez, Anand Vaidya and Mireille E. Schnitzer
Abstract
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 http://watzilei.com/shiny/collider/.
Keywords:
epidemiological methods, causality, noncommunicable disease epidemiology
This repository provides free open access for replicability and educational purposes to the data and code used in the educational note:
- app.R
- code_boxes_article.R
Please cite this repository as follows:
Miguel Angel Luque-Fernandez, Michael Schomaker, Daniel Redondo-Sanchez, Maria Jose Sanchez Perez, Anand Vaidya and Mireille E. Schnitzer (2018). Educational Note: Paradoxical Collider Effect in the Analysis of Non-Communicable Disease Epidemiological Data: a reproducible illustration and web application. GitHub Repository: https://github.com/migariane/ColliderApp
Figure. Collider web application 2018.
Acknowledgment: Miguel Angel Luque Fernandez is supported by the Spanish National Institute of Health, Carlos III Miguel Servet I Investigator Award (CP17/00206). Maria Jose Sanchez Perez is supported by the Andalusian Department of Health. Research, Development and Innovation Office project grant PI-0152/2017. Anand Vaidya was supported by the National Institutes of Health (grants DK107407 and DK115392) and by the Doris Duke Charitable Foundation (award 2015085). Mireille E. Schnitzer is supported by a New Investigator Salary Award from the Canadian Institutes of Health Research.