Implications of nondifferential misclassification on estimates of attributable risk

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.

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.

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.

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.