Katsiaryna Bykov1, Shirley V Wang1, Jesper Hallas2,3, Anton Pottegård2, Malcolm Maclure4, Joshua J Gagne1. 1. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. 2. Clinical Pharmacology and Pharmacy, Department of Public Health, University of Southern Denmark, Odense, Denmark. 3. Department of Clinical Biochemistry and Clinical Pharmacology, Odense University Hospital, Odense, Denmark. 4. Department of Anesthesiology, Pharmacology and Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Abstract
PURPOSE: The 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. METHODS: We 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. RESULTS: Case-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. CONCLUSIONS: Investigators 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.
PURPOSE: The 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. METHODS: We 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. RESULTS: Case-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. CONCLUSIONS: Investigators 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.
Authors: Malcolm Maclure; Bruce Fireman; Jennifer C Nelson; Wei Hua; Azadeh Shoaibi; Antonio Paredes; David Madigan Journal: Pharmacoepidemiol Drug Saf Date: 2012-01 Impact factor: 2.890
Authors: Jessica M Franklin; Sebastian Schneeweiss; Jennifer M Polinski; Jeremy A Rassen Journal: Comput Stat Data Anal Date: 2014-04 Impact factor: 1.681
Authors: Shahar Shmuel; Virginia Pate; Marc J Pepin; Janine C Bailey; Yvonne M Golightly; Laura C Hanson; Til Stürmer; Rebecca B Naumann; Danijela Gnjidic; Jennifer L Lund Journal: J Am Geriatr Soc Date: 2021-07-22 Impact factor: 5.562