The CBER BEST Seminar Series hosted by the OHDSI Team at Northeastern’s Roux Institute will have our very own OHDSI Titan, Dr. Fan Bu kick off the 2023 series on Wednesday, February 8 from 11:00AM-12:00 PM ET on the topic of “Bayesian Safety Surveillance with Adaptive Bias Correction”. (A follow-up to her incredible 2022 OHDSI Global Symposium presentation)
Description:
In this presentation, we will discuss a collaborative project with the FDA CBER BEST Initiative to improve on post-market vaccine safety surveillance procedures through Bayesian sequential analysis. Post-market surveillance on approved vaccine products is essential for addressing safety concerns. The goal is to detect rare or high-risk adverse events that often go undetected in clinical trials due to limited sample sizes. At the FDA, safety surveillance is performed by sequential analysis of real-world observational healthcare data that accrue over time. The major challenge is that we need to control the testing error induced by sequential multiplicity, while being able to detect safety signals rapidly. Meanwhile, observational data are often systematically biased, which can substantially inflate decision error. The standard statistical approach for surveillance is Maximum Sequential Probability Ratio Test (MaxSPRT). It is designed to handle sequential multiplicity, but it requires a pre-fixed surveillance schedule and does not provide a coherent framework for adjusting for systematic bias. Collaborating with FDA CBER, we have developed a Bayesian alternative surveillance procedure that tackles these challenges in sequential analysis of observational data. Through comprehensive empirical evaluations on large-scale observational healthcare databases, we show that, compared to MaxSPRT, our Bayesian method offers more flexibility on the surveillance schedule, more transparency and interpretability in decision-making, and better error control through statistical correction of bias in observational data.
Presenter:
Dr. Fan Bu
Fan Bu obtained her PhD in Statistics from Duke University and is currently a Postdoctoral Researcher at the Department of Biostatistics at UCLA. She is interested in developing novel statistical and computational tools for complex structures in modern data formats, especially in generating reliable and reproducible evidence from real-world data in public health. During the last year, she has been one of the leading investigators for the FDA BEST contract with OHDSI.