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OHDSI Center Events

CBER BEST Seminar Series: Quantitative Bias Analysis Methods to Improve Inferences
Seminar

CBER BEST Seminar Series: Quantitative Bias Analysis Methods to Improve Inferences

Sep 07

11:00 am12:00 pm

placeNortheastern

Anyone can register and join for free.

The CBER BEST Initiative Seminar Series is designed to share and discuss recent research of relevance to ongoing and future surveillance activities of CBER-regulated products, namely biologics. The series focuses on safety and effectiveness of biologics including vaccines, blood components, blood-derived products, tissues and advanced therapies. The seminars will provide information on characteristics of biologics, required infrastructure, study designs, and analytic methods utilized for pharmacovigilance and pharmacoepidemiologic studies of biologics. They will also cover information regarding potential data sources, informatics challenges and requirements, utilization of real-world data and evidence, and risk-benefit analysis for biologic products. The length of each session may vary, and the presenters will be invited from outside FDA. Please see the details below for our upcoming seminar. 

Topic

Quantitative Bias Analysis Methods to Improve Inferences

Speaker

Matthew P. Fox
Professor, Boston University School of Public Health

Description

Observational epidemiologic research around vaccine efficacy and safety can provide important insights into causal relationships, but key sources of bias often impair the inferences we draw from these studies. Uncontrolled confounding, selection bias and information bias are common in epidemiologic research and failure to account for their impacts in a quantitative manner (rather than qualitative assessments in discussion sections of manuscripts after conclusions have been drawn) can led to poor inferences. This talk will give an overview of quantitative bias analysis methods to demonstrate how they can be implemented on both summary and record-level data to account for the impact of systematic error on study results and provide some examples using data from the literature on how to apply these methods to vaccines safety data.

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