Leveraging the entire cohort in drug safety monitoring: part 1 methods for sequential surveillance that use regression adjustment or weighting to control confounding in a multisite, rare event, distributed data setting

Abstract

Objective: Study designs involving self-controlled or exposure-matched samples are commonly used to monitor postmarket vaccine and drug safety, and they use a subset of the available larger cohort. This article overviews group sequential methods designed for observational data safety monitoring that use the whole exposed and unexposed cohorts by implementing regression adjustment or weighting to control confounding.

Methods: We summarize what is known about the performance of "whole cohort" methods in multisite health plan data networks such as the Sentinel System of the Food and Drug Administration, where outcomes are rare, individual-level patient data cannot be pooled across sites, site heterogeneity is large, and data are dynamically updated over time.

Results: Group sequential estimation and testing methods that use regression or weighting can flexibly handle electronic health care data's unpredictability, including an uncertain rate of new product uptake, variable composition of the population over time, and data changes due to dynamic administrative updates. Regression and weighting methods generally have higher power, faster signal detection, and fewer practical challenges compared with some design-based confounder adjustment methods.

Conclusion: Group sequential regression adjustment and weighting approaches are feasible and underused in practice. They leverage more information than designs that involved sampling and increase power to detect rare adverse effects without increasing bias.

Keywords: Active surveillance; Distributed databases; Drug safety; Group sequential; Pharmacoepidemiology; Statistical methods.