Extreme restriction design as a method for reducing confounding by indication in pharmacoepidemiologic research.

Abstract

Confounding by indication is a concern in observational pharmacoepidemiologic studies, including those that use active comparator, new user (ACNU) designs. Here, we present a method of restriction to an indication, which we call "extreme restriction," to reduce confounding in such studies. As a case study, we evaluated the effect of proton pump inhibitors (PPIs) on hospitalization for community-acquired pneumonia (HCAP). PPI use has been associated with increased HCAP risk, but this association likely results from confounding by indication due to gastroesophageal reflux disease (GERD). Using the UK's Clinical Practice Research Datalink, we compared the risk of HCAP within 180days between PPI users and histamine-2 receptor antagonist (H2RA) users in an ACNU cohort using Cox proportional hazard models with a time-fixed exposure definition adjusted for high-dimensional propensity score deciles. We then performed the same analysis on an "extremely-restricted" cohort of incident nonsteroidal anti-inflammatory drug (NSAID) users, some of whom received PPIs for prophylaxis. Because PPIs were given as prophylaxis in this population, confounding due to GERD should be limited. We compared effect estimates between ACNU and restricted cohorts to evaluate confounding in both analyses. In the ACNU cohort, PPIs were associated with an increased risk of HCAP (hazard ratio [HR]: 1.25; 95% confidence interval [CI]: 1.05, 1.47), but this association was not present in the restricted cohort (HR: 1.06; 95% CI: 0.75, 1.49). Restriction to a single indication for treatment may reduce confounding by indication in studies conducted in distributed data networks and other large databases.