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Drug exposure misclassification in pharmacoepidemiology: Sources and relative impact
Mirjam Hempenius1, Rolf H H Groenwold2, Anthonius de Boer1
1Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.
Background:
Drug exposure assessment based on dispensing data can be misclassified when patients do not adhere to their therapy or when information about over-the-counter drugs is not captured in the study database. Previous research has considered hypothetical sensitivity and specificity values, whereas this study aims to assess the impact of literature-based real values of exposure misclassification.
Methods:
A synthetic cohort study was constructed based on the proportion of exposure theoretically captured in a database (range 0.5-1.0) and the level of adherence (0.5-1.0). Three scenarios were explored: nondifferential misclassification, differential misclassification (misclassifications dependent on an unmeasured risk factor doubling the outcome risk), and nondifferential misclassification in a comparative effectiveness study (RRA and RRB both 2.0 compared to nonuse, RRA-B 1.0).
Results:
For the scenarios with nondifferential misclassification, 25% nonadherence or 25% uncaptured exposure changed the RR from 2.0 to 1.75, and 1.95, respectively. Applying different proportions of nonadherence or uncaptured use (20% vs. 40%) for subgroups with and without the risk factor, an RR of 0.95 was observed in the absence of a true effect (i.e., true RR = 1). In the comparative effectiveness study, no effect on RR was seen for different proportions of uncaptured exposure; however, different levels of nonadherence for the drugs (20% vs. 40%) led to an underestimation of RRA-B (0.89).
Discussion:
All scenarios led to biased estimates, but the magnitude of the bias differed across scenarios. When testing the robustness of findings of pharmacoepidemiologic studies, we recommend using realistic values of nonadherence and uncaptured exposure based on real-world data.
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