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Collider bias is a distortion of an exposure-outcome association caused by controlling for a common effect of both. Learn how to identify and prevent collider bias with causal diagrams and examples from Sackett and obesity paradox.
Collider bias is a threat to validity in observational studies and RCTs. It is often less readily recognized than confounding. A study by Valls-Pedret et al 6 illustrates an example of potential collider bias in an RCT.
Berkson's paradox, also known as Berkson's bias, collider bias, or Berkson's fallacy, is a result in conditional probability and statistics which is often found to be counterintuitive, and hence a veridical paradox. It is a complicating factor arising in statistical tests of proportions.
18 de feb. de 2022 · So-called confounders are well known, but distortion by collider bias (CB) has received little attention in medical research to date. The goal of this article is to present the principle of CB, and measures that can be taken to avoid it, by way of a few illustrative examples.
12 de nov. de 2020 · In this paper, we discuss why collider bias should be of particular concern to observational studies of COVID-19 infection and disease risk, and show how sample selection can lead to dramatic...
10 de oct. de 2023 · Collider stratification bias is a threat to validity of causal inferences in epidemiology. It occurs when conditioning on a common effect of 2 otherwise unrelated factors creates a spurious or distorted association between them. Learn the structure, causes, and scenarios of collider stratification bias using directed acyclic graphs and examples.
2 de may. de 2022 · Collider bias is a form of selection bias that arises when the investigator controls for a variable (the collider) that occurs after the exposure and outcome. The exposure and outcome can both independently create a collider variable.
