Addressing bias due to measurement error of an outcome with unknown sensitivity in database epidemiologic studies. A contribution from the ConcePTION project.

Abstract

In epidemiologic database studies, the occurrence of an event is measured with error through an indicator whose specificity is often maximized, 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 nondifferential sensitivity. Although direct estimation of sensitivity is often infeasible, this novel methodology improves evidence based on data obtained from reuse of existing databases, which may prove critical for regulatory and public health decisions.