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 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/].

中文翻译：

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教育注：非传染性疾病流行病学数据分析中的对撞机效应：可重现的插图和网络应用

古典流行病学集中于控制混杂，但是直到最近，流行病学专家才开始关注对撞机产生的偏见。某些变量对（例如，结果Y和暴露A）的对撞器是两者共同引起的第三个变量（C）。在有向无环图（DAG）中，对撞机是倒叉中间的变量（即A→C←Y中的变量C）。控制或调节对撞机的分析（即通过分层或回归）可能会在其原因之间引入虚假关联。这可能解释了医学文献中的许多悖论性发现，其中针对特定结局的既定危险因素似乎具有保护性。我们使用非传染性疾病流行病学中的一个实例来进行上下文描述并解释对撞机条件的影响。我们生成了一个具有1000个观测值的数据集，并进行了蒙特卡洛模拟，以评估24小时饮食中钠的摄入对收缩压的影响，控制年龄（作为混杂因素）和24小时尿蛋白排泄（对行为的影响）作为对撞机。我们说明了如何将对撞机添加到回归模型中会引入偏差。因此，为防止自相矛盾，流行病学家估计因果关系应谨慎对待对撞机。我们在整篇文章的易于阅读的框中提供了R代码，并提供了一个GitHub存储库[https://github.com/migariane/ColliderApp]供读者重现我们的示例。