Varied forms of bias due to nondifferential error in measuring exposure

Continuous exposure variables are frequently categorized in epidemiologic data analysis. It has recently been shown that such categorization may transform nondifferential error in measuring continuous exposure variables into differential exposure misclassification. This paper assesses the direction and magnitude of the resulting misclassification bias under a variety of practically relevant forms of nondifferential measurement error. The expected bias of measures of the exposure-disease association is toward the null in the case of purely random measurement error with a mean of zero. Systematic nondifferential over- or underestimation of the exposure may bias measures of the exposure-disease association either toward the null or away from the null, depending on the underlying distribution of exposure, the true exposure-disease relation, and the cutpoints employed for categorization. If exposure measurement error has both random and systematic components, the direction of the net bias is less predictable than with pure error of either type, but bias toward the null is increasingly likely as the random component grows larger. The results indicate the need for careful evaluation of potential effects of nondifferential exposure measurement error in epidemiologic studies in which categories are formed from continuous exposure variables.