Confounding bias arises when the disease-exposure relationship is disturbed by an extraneous factor called the confounding variable.

The confounding variable is not actually involved in the exposure-disease relationship. It is however predictive of disease but is unequally distributed between exposure groups.

Being related both to the disease and the risk factor, the confounding variable could lead to a spurious apparent relation between disease and exposure if it is a factor in the selection of subjects into the study.

A confounder must fulfil the following criteria: relation to both disease and exposure, not being part of the causal pathway, being a true risk factor for the disease, being associated to the exposure in the source population, and being not affected by either disease or exposure.

Prevention of confounding at the design stage by eliminating the effect of the confounding factor can be achieved using 4 strategies: pair-matching, stratification, randomisation, and restriction.

Confounding can be treated at the analysis stage by various adjustment methods (both non-multivariate and multi-variate). Non-multivariate treatment of confounding employs standardization and stratified Mantel-Haenszel analysis.

Multi-variate treatment of confounding employs multivariate adjustment procedures: multiple linear regression, linear discriminant function, and multiple logistic regression.

Care must be taken to deal only with true confounders. Adjusting for non-confounders reduces the precision of the study.