The authors present a technique for correcting for exposure measurement error in the analysis of case-control data when subjects have a variable number of repeated measurements, and the average is used as the subject's measure of exposure. The true exposure as well as the measurement error are assumed to be normally distributed. The method transforms each subject's observed average by a factor which is a function of the measurement error parameters, prior to fitting the logistic regression model. The resulting logistic regression coefficient estimate based on the transformed average is corrected for error. A bootstrap method for obtaining confidence intervals for the true regression coefficient, which takes into account the variability due to estimation of the measurement error parameters, is also described. The method is applied to data from a nested case-control study of hormones and breast cancer.