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Fan Bu1,2, Martijn J Schuemie1,3, Akihiko Nishimura4

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A new Bayesian surveillance procedure improves vaccine safety monitoring by reducing bias and multiple testing errors. This method offers faster signal detection and more accurate estimations compared to the standard approach.

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Area of Science:

  • Pharmacovigilance
  • Biostatistics
  • Real-world evidence analysis

Background:

  • Postmarket vaccine safety surveillance is crucial for mass vaccination programs.
  • Existing methods like maximized sequential probability ratio test (MaxSPRT) face challenges with multiple testing and confounding bias.
  • The rigid framework of MaxSPRT necessitates prespecified surveillance schedules.

Purpose of the Study:

  • To develop a flexible Bayesian surveillance procedure addressing bias and multiple testing in vaccine safety monitoring.
  • To mitigate bias by analyzing negative control outcomes within a Bayesian hierarchical model.
  • To enhance flexibility and sequential detection of safety signals using updated posterior probabilities.

Main Methods:

  • Developed a Bayesian surveillance procedure incorporating an empirical bias distribution from negative control outcomes.
  • Employed a Bayesian hierarchical model to estimate vaccine effects on adverse events.
  • Utilized updated posterior probabilities for sequential safety signal detection.
  • Evaluated the procedure against MaxSPRT using six US observational healthcare databases (over 360 million patients) and two epidemiological designs (historical comparator, self-controlled case series).

Main Results:

  • The proposed Bayesian procedure substantially reduced Type 1 error rates compared to MaxSPRT.
  • It maintained high statistical power and achieved faster safety signal detection.
  • Estimation accuracy for vaccine effects on adverse events was considerably improved.
  • The empirical evaluation involved over 7 million result sets.

Conclusions:

  • The novel Bayesian surveillance procedure offers a more flexible and accurate alternative to MaxSPRT for postmarket vaccine safety surveillance.
  • It effectively addresses key challenges of bias and multiple testing, enhancing the reliability of real-world safety data analysis.
  • Open-source R package 'EvidenceSynthesis' and an R ShinyApp are available for method implementation and result visualization.