<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>collider on MALF</title><link>https://maluque.netlify.app/tags/collider/</link><description>Recent content in collider on MALF</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>&amp;copy; 2025-2026 Miguel Angel Luque-Fernandez</copyright><lastBuildDate>Tue, 21 Aug 2018 00:00:00 +0000</lastBuildDate><atom:link href="https://maluque.netlify.app/tags/collider/index.xml" rel="self" type="application/rss+xml"/><item><title>Colliders in Epidemiology: an educational interactive web application</title><link>https://maluque.netlify.app/project/collider/</link><pubDate>Tue, 21 Aug 2018 00:00:00 +0000</pubDate><guid>https://maluque.netlify.app/project/collider/</guid><description>A collider for a certain pair of variables (outcome and exposure) is a third variable that is influenced by both of them. Controlling for, or conditioning the analysis on (i.e., stratification or regression) a collider, can introduce a spurious association between its causes (exposure and outcome) potentially explaining why the medical literature is full of paradoxical findings [6]. In DAG terminology, a collider is the variable in the middle of an inverted fork (i.e., variable W in A -&amp;gt; W &amp;lt;- Y). While this methodological note will not close the vexing gap between correlation and causation, but it will contribute to the increasing awareness and the general understanding of colliders among applied epidemiologists and medical researchers.</description></item></channel></rss>