Standardizing to specific target populations in distributed networks and multisite pharmacoepidemiologic studies

Abstract

Distributed networks and other multi-site studies assess drug safety and effectiveness in diverse populations by pooling information. Targeting groups of clinical or policy interest (including specific sites or site combinations) and applying weights based on effect measure modifiers (EMMs) prior to pooling estimates within multi-site studies may increase interpretability and improve precision. We simulated a four-site study, standardized each site using inverse odds weights (IOW) to resemble the three smallest sites or the smallest site, estimated IOW-weighted RDs, and combined estimates with inverse variance weights (IVW). We also created an artificial distributed network in the Clinical Practice Research Datalink (CPRD) Aurum consisting of one site for each geographic region. We compared metformin and sulfonylurea initiators with respect to mortality, targeting the smallest region. In the simulation, IOW reduced differences between estimates and increased precision when targeting the three smallest sites or the smallest site. In the CPRD study, the IOW + IVW estimate was also more precise (smallest region RD and 95% CI: 5.41%, 1.03%-9.79%), IOW+IVW RD and 95% CI: 3.25%, 3.07%-3.43%). When performing pharmacoepidemiologic research in distributed networks or multi-site studies in the presence of EMMs, designating target populations has the potential to improve estimate precision and interpretability.