Summary
Objectives: Only the effects of isolated nondifferential misclassification of exposure or disease on the estimates of attributable risk have been discussed in the literature.
The aim of this paper is to broaden the spectrum of scenarios for which implications
of misclassification are available.
Methods: For this purpose, a matrix-based approach allowing a comprehensive, unified analysis
of various structures of misclassification is introduced. The relative bias or – in
the situation of covariate misclassification – the relative adjustment are presented
for the different misclassification scenarios.
Results: Under nondifferential misclassification of exposure or disease, the attributable
risk is biased towards the null with the only exception of perfect sensitivity of
exposure classification or perfect specificity of disease classification both leading
to an unbiased attributable risk. From these two marginal effects, the consequences
of simultaneous nondifferential independent misclassification of exposure and disease on the attributable risk are derived in the matrix-based approach. Misclassification
of a dichotomous covariate leads to partial adjustment.
Conclusions: To a large extent, the results for the attributable risk are in accordance with the
well-known results for the relative risk. The algebraic differences between the two
risk measures, however, make it necessary to repeat the methodological considerations
for the attributable risk.
Keywords
Epidemiology - epidemiologic methods - bias - confounding factors (epidemiology) -
effect modifiers (epidemiology)
