Selection bias requires selection: The case of collider stratification bias

• Autores: Haidong Lu, Gregg S Gonsalves, Daniel Westreich
• Localización: American journal of epidemiology, ISSN-e 1476-6256, ISSN 0002-9262, Vol. 193, Nº. 3, 2024, págs. 407-409
• Idioma: inglés
• Enlaces
  • Texto completo  1    2 (pdf) 
• Resumen

  • Abstract In epidemiology, collider stratification bias, the bias resulting from conditioning on a common effect of two causes, is oftentimes considered a type of selection bias, regardless of the conditioning methods employed. In this commentary, we distinguish between two types of collider stratification bias: collider restriction bias due to restricting to one level of a collider (or a descendant of a collider) and collider adjustment bias through inclusion of a collider (or a descendant of a collider) in a regression model. We argue that categorizing collider adjustment bias as a form of selection bias may lead to semantic confusion, as adjustment for a collider in a regression model does not involve selecting a sample for analysis. Instead, we propose that collider adjustment bias can be better viewed as a type of overadjustment bias. We further provide two distinct causal diagram structures to distinguish collider restriction bias and collider adjustment bias. We hope that such a terminological distinction can facilitate easier and clearer communication.