A scan statistic for identifying optimal risk windows in vaccine safety studies using self‐controlled case series design. (10th January 2013)

• Record Type: Journal Article
• Authors: Xu, Stanley; Hambidge, Simon J.; McClure, David L.; Daley, Matthew F.; Glanz, Jason M.
• Is Part Of: Statistics in medicine. Volume 32:Number 19(2013)
• Page Start: 3290
• Page End: 3299
• Publication Date: 2013-01-10
• Subjects: self‐controlled case series -- adverse events after vaccination -- fixed‐effects model -- scan statistics -- maximum likelihood ratio test; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.5733
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract: In the examination of the association between vaccines and rare adverse events after vaccination in postlicensure observational studies, it is challenging to define appropriate risk windows because prelicensure RCTs provide little insight on the timing of specific adverse events. Past vaccine safety studies have often used prespecified risk windows based on prior publications, biological understanding of the vaccine, and expert opinion. Recently, a data‐driven approach was developed to identify appropriate risk windows for vaccine safety studies that use the self‐controlled case series design. This approach employs both the maximum incidence rate ratio and the linear relation between the estimated incidence rate ratio and the inverse of average person time at risk, given a specified risk window. In this paper, we present a scan statistic that can identify appropriate risk windows in vaccine safety studies using the self‐controlled case series design while taking into account the dependence of time intervals within an individual and while adjusting for time‐varying covariates such as age and seasonality. This approach uses the maximum likelihood ratio test based on fixed‐effects models, which has been used for analyzing data from self‐controlled case series design in addition to conditional Poisson models. Copyright © 2013 John Wiley & Sons, Ltd.