Serially Combining Epidemiological Designs Does Not Improve Overall Signal Detection in Vaccine Safety Surveillance

Drug Saf. 2023 Aug;46(8):797-807. doi: 10.1007/s40264-023-01324-1. Epub 2023 Jun 16.

Abstract

Introduction: Vaccine safety surveillance commonly includes a serial testing approach with a sensitive method for 'signal generation' and specific method for 'signal validation.' The extent to which serial testing in real-world studies improves or hinders overall performance in terms of sensitivity and specificity remains unknown.

Methods: We assessed the overall performance of serial testing using three administrative claims and one electronic health record database. We compared type I and II errors before and after empirical calibration for historical comparator, self-controlled case series (SCCS), and the serial combination of those designs against six vaccine exposure groups with 93 negative control and 279 imputed positive control outcomes.

Results: The historical comparator design mostly had fewer type II errors than SCCS. SCCS had fewer type I errors than the historical comparator. Before empirical calibration, the serial combination increased specificity and decreased sensitivity. Type II errors mostly exceeded 50%. After empirical calibration, type I errors returned to nominal; sensitivity was lowest when the methods were combined.

Conclusion: While serial combination produced fewer false-positive signals compared with the most specific method, it generated more false-negative signals compared with the most sensitive method. Using a historical comparator design followed by an SCCS analysis yielded decreased sensitivity in evaluating safety signals relative to a one-stage SCCS approach. While the current use of serial testing in vaccine surveillance may provide a practical paradigm for signal identification and triage, single epidemiological designs should be explored as valuable approaches to detecting signals.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Databases, Factual
  • Electronic Health Records
  • Humans
  • Research Design
  • Sensitivity and Specificity
  • Vaccines* / adverse effects

Substances

  • Vaccines