Large-scale empirical identification of candidate comparators for pharmacoepidemiological studies

Objectives The 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. Methods We 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. Results Across 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. Conclusion Empirical 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by Janssen Research & Development. All authors are employees or contractors of Janssen Pharmaceuticals and may hold stock or stock options in Johnson & Johnson. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used data from five sources of insurance claims: Merative MarketScan Commercial Claims and Encounters (CCAE), Merative MarketScan Multi-State Medicaid (MDCD), Merative MarketScan Medicare Supplemental (MDCR), Japan Medical Data Center (JMDC), and Optum De-Identified Clinformatics Data Mart. All of this data is commercially available from the aforementioned vendors. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All results in the present study, as well as those from additional data sources not originally included in the study, are available in an interactive web application.