Applying sequential surveillance methods that use regression adjustment or weighting to control confounding in a multisite, rare-event, distributed setting: Part 2 in-depth example of a reanalysis of the measles-mumps-rubella-varicella combination vaccine and seizure risk

Abstract

Objective: In-depth example of two new group sequential methods for postmarket safety monitoring of new medical products.

Study design and setting: Existing trial-based group sequential approaches have been extended to adjust for confounders, accommodate rare events, and address privacy-related constraints on data sharing. Most adaptations have involved design-based confounder strategies, for example, self-controlled or exposure matching, while analysis-based approaches like regression and weighting have received less attention. We describe the methodology of two new group sequential approaches that use analysis-based confounder adjustment (GS GEE) and weighting (GS IPTW). Using data from the Food and Drug Administration's Sentinel network, we apply both methods in the context of a known positive association: the measles-mumps-rubella-varicella vaccine and seizure risk in infants.

Results: Estimates from both new approaches were similar and comparable to prior studies using design-based methods to address confounding. The time to detection of a safety signal was considerably shorter for GS IPTW, which estimates a risk difference, compared to GS GEE, which provides relative estimates of excess risk.

Conclusion: Future group sequential safety surveillance efforts should consider analysis-based confounder adjustment techniques that evaluate safety signals on the risk difference scale to achieve greater statistical power and more timely results.

Keywords: Active surveillance; Distributed databases; Electronic health record (EHR); Group sequential analysis; Rare events; Vaccine safety.