External validation, repeat determination, and precision of risk estimation in misclassified exposure data in epidemiology

Abstract

Study objective - The aim was to quantify the difference in precision of risk estimates in epidemiology between the situations where misclassification of exposure is corrected for by external validation and where it is corrected for by internal repeat measurement. Precision was measured in terms of the expected width of the 95% confidence interval on the odds ratio. Design - In a hypothetical case-control study, first with 100 cases and 100 controls, then with 100 cases and 1000 controls (the latter to approximate the cohort study situation), expected estimated odds ratios and confidence intervals were calculated based on postulated underlying true odds ratios and misclassification error rates. The sizes of the confidence intervals using the two design strategies were compared, based on the same number of subjects receiving internal repeat measurements as were used in the external validation study. Main results - Confidence intervals obtained using internal repeat measurement were considerably narrower than those using external validation. Both methods yielded approximately correct point estimates. Conclusions - In terms of precision, it is preferable to correct for misclassification using internal repeat measurement rather than external validation.