Correction of Logistic Regression Relative Risk Estimates and Confidence Intervals for Random Within-Person Measurement Error

Frequently, covariates used in a logistic regression are measured with error. The authors previously described the correction of logistic regression relative risk estimates for measurement error in one or more covariates when a “gold standard” is available for exposure assessment. For some exposures (e.g., serum cholesterol), no gold standard exists, and one must assess measurement error via a reproducibiiity substudy. In this paper, the authors present measurement error methods for logistic regression when there is error (possibly correlated) in one or more covariates and one has data from both a main study and a reproducibiiity substudy. Confidence intervals from this procedure reflect error in parameter estimates from both studies. These methods are applied to the Framingham Heart Study, where the 10–year incidence of coronary heart disease is related to several coronary risk factors among 1, 731 men disease-free at examination 4. Reproducibiiity data are obtained from the subgroup of 1, 346 men seen at examinations 2 and 3. Estimated odds ratios comparing extreme quintiles for risk factors with substantial error were increased after correction for measurement error (serum cholesterol, 2.2 vs. 2.9; serum glucose, 1.3 vs. 1.5; systolic blood pressure, 2.8 vs. 3.8), but were generally decreased or unchanged for risk factors with little or no error (body mass index, 1.6 vs. 1.6; age 65–69 years vs. 35–44 years, 4.3 vs. 3.8; smoking, 1.7 vs. 1.7). Am J Epidemiol 1992; 136: 1400–13.