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.
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.
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.
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: firstname.lastname@example.org