title: Signal detection of adverse events with imperfect confirmation rates in vaccine safety studies using self-controlled case series design.
abstract: The Vaccine Safety Datalink project captures electronic health record data including vaccinations and medically attended adverse events on 8.8 million enrollees annually from participating managed care organizations in the United States. While the automated vaccination data are generally of high quality, a presumptive adverse event based on diagnosis codes in automated health care data may not be true (misclassification). Consequently, analyses using automated health care data can generate false positive results, where an association between the vaccine and outcome is incorrectly identified, as well as false negative findings, where a true association or signal is missed. We developed novel conditional Poisson regression models and fixed effects models that accommodate misclassification of adverse event outcome for self-controlled case series design. We conducted simulation studies to evaluate their performance in signal detection in vaccine safety hypotheses generating (screening) studies. We also reanalyzed four previously identified signals in a recent vaccine safety study using the newly proposed models. Our simulation studies demonstrated that (i) outcome misclassification resulted in both false positive and false negative signals in screening studies; (ii) the newly proposed models reduced both the rates of false positive and false negative signals. In reanalyses of four previously identified signals using the novel statistical models, the incidence rate ratio estimates and statistical significances were similar to those using conventional models and including only medical record review confirmed cases.
authors.affiliation: The Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, 80231, USA.
authors.forename: Stanley
authors.initials: S
authors.lastname: Xu
authors.forename: Sophia
authors.initials: S
authors.lastname: Newcomer
authors.forename: Jennifer
authors.initials: J
authors.lastname: Nelson
authors.forename: Lei
authors.initials: L
authors.lastname: Qian
authors.forename: David
authors.initials: D
authors.lastname: McClure
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
authors.forename: Chan
authors.initials: C
authors.lastname: Zeng
authors.forename: Jason
authors.initials: J
authors.lastname: Glanz
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D002675:Child, Preschool; D006801:Humans; D007252:Influenza Vaccines / Q000009:adverse effects; D015233:Models, Statistical; D012449:Safety; D014612:Vaccines / Q000009:adverse effects*; D015164:Vaccines, Inactivated / Q000009:adverse effects
chemical_list: D007252:Influenza Vaccines; D014612:Vaccines; D015164:Vaccines, Inactivated
keywords: Conditional Poisson model; Fixed effects model; Misclassification of adverse events; Screening studies; Self-controlled case series