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Comparison of self-controlled designs for evaluating outcomes of drug-drug interactions

simulation study

Bykov, Katsiaryna1; Franklin, Jessica M.1; Li, Hu2; Gagne, Joshua J.1

doi: 10.1097/EDE.0000000000001087
Original Article: PDF Only

Background: Self-controlled designs, both case–crossover and self-controlled case series, are well suited for evaluating outcomes of drug–drug interactions in electronic healthcare data. Their comparative performance in this context, however, is unknown.

Methods: We simulated cohorts of patients exposed to two drugs: a chronic drug (object) and a short-term drug (precipitant) with an associated interaction of 2.0 on the odds ratio scale. We analyzed cohorts using case–crossover and self-controlled case series designs evaluating exposure to the precipitant drug within person–time exposed to the object drug. Scenarios evaluated violations of key design assumptions: (1) time-varying, within-person confounding; (2) time trend in precipitant drug exposure prevalence; (3) non-transient precipitant exposure; and (4) event-dependent object drug discontinuation.

Results: Case–crossover analysis produced biased estimates when 30% of patients persisted on the precipitant drug (estimated OR 2.85) and when the use of the precipitant drug was increasing in simulated cohorts (estimated OR 2.56). Self-controlled case series produced biased estimates when patients discontinued the object drug following the occurrence of an outcome (estimated incidence ratio (IR) of 2.09 [50% of patients stopping therapy] and 2.22 [90%]. Both designs yielded similarly biased estimates in the presence of time-varying, within-person confounding.

Conclusion: In settings with independent or rare outcomes and no substantial event-dependent censoring (<50%), self-controlled case series may be preferable to case–crossover design for evaluating outcomes of drug–drug interactions. With heavy event-dependent drug discontinuation, a case–crossover design may be preferable provided there are no time-related trends in drug exposure.

1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

2 Eli Lilly and Company, Indianapolis, IN

Conflict of Interest Disclosures: K. Bykov has received support from a doctoral training grant from Takeda to Harvard T.H. Chan School of Public Health. J.J. Gagne was Principal Investigator of a grant from Novartis Pharmaceuticals Corporation to the Brigham and Women's Hospital and is a consultant to Aetion Inc. and to Optum, Inc, all for unrelated work. H. Li is an employee of Lilly and Company, of which she also own equity.

Source of Funding: This study was funded through Lilly Research Award Program. K. Bykov is supported by training grant from the National Institute of Child Health and Human Development (T32 HD40128-14).

Data sharing: Computing code is presented in the eAppendix.

Acknowledgments: The authors would like to thank Sangmi Kim and Jill Chappell for their contributions to interpretation of results.

Corresponding author: Katsiaryna Bykov, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, 1620 Tremont St., Ste.3030, Boston MA, 02120; Phone: 617-278-0930; e-mail:

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