AbstractPurposeThe case‐crossover design is increasingly used to evaluate the effects of chronic medications; however, as traditionally implemented in pharmacoepidemiology, with referent period preceding the outcome, it may lead to bias in the presence of persistent exposures. We aimed to evaluate the extent and magnitude of bias in case‐crossover analyses of chronic and persistent exposures, using simulations.MethodsWe simulated cohorts with either 30‐day, 180‐day, or 2‐year exposure duration; and with varying degrees of persistence (10%, 30%, 50%, 70%, or 90% of patients not stopping exposure). We evaluated all scenarios under the null and the scenario with 30% persistence under varying exposure effects (odds ratios of 0.25 to 4.0). Cohorts were analyzed using conditional logistic regression that compared the odds of exposure on the outcome day to the odds of exposure on a referent day 30 days prior to the outcome. We further implemented the case‐time‐control design to evaluate its ability to adjust for bias from persistence.ResultsCase‐crossover analyses produced unbiased estimates across all scenarios without persistent users, regardless of exposure duration. In scenarios where some patients persisted on treatment, case‐crossover analyses resulted in upward bias, which increased with increasing proportion of persistent users, but did not vary substantially in relation to the magnitude of the true effect. Case‐time‐control analyses removed bias in all scenarios.ConclusionsInvestigators should be aware of bias due to treatment persistence in unidirectional case‐crossover analyses of chronic medications, which can be remedied with a control group of similarly persistent noncases.