Example of Misclassification Bias

Outcome Variable - Cohort Study

Lead Author(s): Jeff Martin, MD

Misclassification bias of the outcome variable does not reduce the measure of association.


As you can see in the diagram below the risk ratio is 2.0 in the true scenario.

If specificity of the outcome measurement is 100% This is because all that you have done is to decrease both this cell and this cell by the same percentage. This little trick is worth knowing about when you are using cutoffs for continuous variables using ROC curves - it is a recommendation to choose cutoffs which provide very high specificity.

Specificity of Outcome 100%

As noted in this figure from Copeland (1977) when specificity is 100%,

ROC Curve with Specificity of Outcome 100%

As shown in the figure below 100% specificity in the outcome measurement preserves unbiased risk ratios even in the face of less than perfect sensitivity. 0120_outcome6_ROC.JPG

Choosing the Cutoff

Choosing the most specific cutoff or the cutoff associated with 100% specificity will lead to least biased ratio measures of effect.


Copeland, K. T., Checkoway, H., McMichael, A. J., & Holbrook, R. H. (1977). Bias due to misclassification in the estimation of relative risk. Am J Epidemiol, 105 (5), 488-495.