Large-scale empirical identification of candidate comparators for pharmacoepidemiological studies

Abstract

ABSTRACTObjectivesThe active comparator new user (ACNU) cohort design has emerged as a best practice for the estimation of drug effects from observational data. However, despite its advantages, this design requires the selection and evaluation of comparators for appropriateness, a process which can be challenging, especially in the context of many drugs. In this paper, we introduce an empirical approach to rank candidate comparators in terms of their similarity to a target drug in high-dimensional covariate space.MethodsWe generated new user cohorts for each RxNorm ingredient in five administrative claims databases, then extracted aggregated pre-treatment covariate data for each cohort across five clinically oriented domains. We formed all pairs of cohorts with ≥ 1,000 patients and computed a scalar similarity score defined as the average of cosine similarities computed within each domain for each pair. Ranked lists of candidate comparators were then generated for each cohort.ResultsAcross up to 1,350 cohorts forming 922,761 comparisons, drugs that were more similar in the ATC hierarchy tended to have higher cohort similarity scores, and the most similar candidate comparators for each of six example drugs consistently corresponded to alternative treatments for the target drug’s indication(s) that could be identified in the literature or publicly registered studies. 80%- 95% of cohorts had at least one comparator with a cohort similarity score ≥ 0.95.ConclusionEmpirical comparator recommendations may serve as a useful aid to investigators and could ultimately enable the automated generation of ACNU-derived evidence, a process that has previously been limited to self-controlled designs.