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.