A probabilistic bias analysis for misclassified categorical exposures, with application to oral anti-hyperglycaemic drugs

Purpose: The effect of drug exposure misclassification generally receives little attention in pharmacoepidemiological research. In this paper, we illustrate a probabilistic bias analysis approach for misclassified categorical exposures and apply it in a database study of oral anti-hyperglycaemic drugs (OADs). Methods: A cohort study based on the Health Search Database general-practice database was carried out by including 12 640 adult (≥40 years) patients newly treated with OADs during 2003–2010. The proportion of days covered by OADs prescriptions during the first year of follow-up was evaluated for each individual, either by means of the prescribed daily dose or the defined daily dose. The effect of misclassification on hypothetical OAD–outcome association profiles was assessed through the proposed probabilistic bias analysis approach, taking advantage of available exposure validation data. Results: During the first year of follow-up, the average (SD) number of months with OADs available was 7 (4) months and 5 (3) months according to the prescribed daily dose and defined daily dose metrics, respectively. Probabilistic bias analysis results based on validation data suggest that the effect of misclassification is complex, as conventional exposure–outcome association estimates may be of greater or lower magnitude than their misclassification-adjusted values. Conclusions: Misclassification should be taken into account in database studies on the safety of prescribed medications. To this aim, investigators should take advantage of external exposure validation data in sensitivity analysis approaches such as ours. Copyright © 2016 John Wiley & Sons, Ltd.