In epidemiological database studies, the occurrence of an event is measured with error through an indicator whose specificity is often maximised, at the expense of sensitivity. However, if the indicator has low sensitivity, measures of occurrence are underestimated. In association studies, risk difference is biased, and risk ratio may be biased as well, in either direction, if the sensitivity is differential across exposure groups. In this work, we show that if an auxiliary screening indicator can be defined to complement the main indicator, estimates of the positive predictive value of both indicators provide tools to estimate the sensitivity of the primary indicator, or a lower bound thereof. This mitigates bias in estimating the number of cases, prevalence, cumulative incidence, rate (particularly when the event is rare), and in association studies, risk ratio and risk difference. They also allow testing for non-differential sensitivity. While direct estimation of sensitivity is often infeasible, this novel methodology improves evidence based on data obtained from re-use of existing databases, which may prove critical for regulatory and public health decisions.
Keywords: adjustment for misclassification; bootstrap test; measurement error; non-differential sensitivity; validity indices.
© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.