Confounding and Confounders

OVERVIEW

Confounding involves error (bias) in the interpretation of an accurate measurement by attributing it to the wrong cause

  • Mill stated that exclusion of all other possible causes (i.e. confounders) is a requirement for establishing causation

CONFOUNDERS

Confounding occurs when there is a relation between a certain characteristic or covariate (C) and group allocation (G) and also between this characteristic and the outcome (O). When the occurs the covariate (C) is termed a confounder.

  • A confounder is prognostic factor – a factor that predicts the outcome of interest
  • Confounders are usually unknown
  • must be unevenly distributed between the comparison groups in a study for confounding to occur
  • A factor is NOT a confounder if it lies on the causal pathway between the variables of interest

COMPARISON WITH MEDIATORS, MODERATORS, AND COVARIATES

Confounders predict the outcome (dependent variable, response, or effect) by interacting with the exposure (cause or independent variable)

  • In kinship terminology, a confounders is a variable that is both an ancestor of the exposure and an ancestor of the outcome (along a path that does not include the exposure)

Whereas:

  • Mediators are part of the causal pathway from exposure to outcome
  • Moderators are interaction terms that change the size or direction (or both) of the effect of the exposure on outcome
  • Covariates are other independent variables that may or may not predict outcomes. A covariate may or may not be confounder.
Slide 3
Diagram showing the relationships between exposure and outcome, in the context of moderators, confounders, mediators, and covariates (Image by Mark Kelson, available from: https://significantlystatistical.wordpress.com/2014/12/12/confounders-mediators-moderators-and-covariates/)

DEALING WITH CONFOUNDERS

Known confounders can be corrected for by ‘adjustment analysis’:

  • adjustment analysis attempts to control for baseline imbalances in important patient characteristics
  • e.g. stratifying results according to known confounders
  • e.g. correcting for known confounders using statistical methods

Unknown confounders

  • typically afflict observational studies, for which propensity score analysis may be used to try to correct for their presence (it’s utility is debatable and remains inferior to randomisation)
  • randomisation, to ensure that confounders are randomly distributed between treatment groups, is the best way to correct for both known and unknown confounders

REFERENCES AND LINKS

Journal articles

  • Pourhoseingholi MA, Baghestani AR, Vahedi M. How to control confounding effects by statistical analysis. Gastroenterol Hepatol Bed Bench. 2012;5(2):79-83. [article]

FOAM and web resources


CCC 700 6

Critical Care

Compendium

Chris is an Intensivist and ECMO specialist at The Alfred ICU, where he is Deputy Director (Education). He is a Clinical Adjunct Associate Professor at Monash University, the Lead for the  Clinician Educator Incubator programme, and a CICM First Part Examiner.

He is an internationally recognised Clinician Educator with a passion for helping clinicians learn and for improving the clinical performance of individuals and collectives. He was one of the founders of the FOAM movement (Free Open-Access Medical education) has been recognised for his contributions to education with awards from ANZICS, ANZAHPE, and ACEM.

His one great achievement is being the father of three amazing children.

On Bluesky, he is @precordialthump.bsky.social and on the site that Elon has screwed up, he is @precordialthump.

| INTENSIVE | RAGE | Resuscitology | SMACC

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.