Deconfounding confounding part 1: traditional explanations
The first article of this series1 presented a framework to assist in judging the presence of bias:
selection bias, or systematic error in how participants are identified or selected;
measurement bias, or systematic error in how variables are measured; and
analytical bias — also known as confounding — or systematic error in the measure of association or conclusion about causation, due to improper or incomplete analysis.
Selection and measurement bias should be managed pre-emptively by good design before the start of the study, but can be detected post hoc by critical appraisal. No statistical method removes the effect of selection or measurement bias post hoc, although there are methods that allow us to model different degrees of bias and evaluate the effect on the measure of association.1 Confounding is slightly different in that it can be adjusted for in the analysis, as long as its sources are understood and measured without too much error.
What is confounding?
A confounder has been traditionally defined as a variable associated with both the exposure and outcome of interest without being an intermediate on the causal pathway between them, which causes a spurious or distorted estimate of the exposure–outcome association. This may be conceptually difficult…