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Waterhouse B, Hartford A, Mukhopadhyay S, et al. Using the Bayesian detection of potential risk using inference on blinded safety data (BDRIBS) method to support the decision to refer an event for unblinded evaluation. Pharm Stat. 2021;doi: 10.1002/pst.2175.
Waterhouse, B., Hartford, A., Mukhopadhyay, S., Ferguson, R., & Hendrickson, B. A. (2021). Using the Bayesian detection of potential risk using inference on blinded safety data (BDRIBS) method to support the decision to refer an event for unblinded evaluation. Pharmaceutical statistics, . https://doi.org/10.1002/pst.2175
Waterhouse, Brian, et al. "Using the Bayesian detection of potential risk using inference on blinded safety data (BDRIBS) method to support the decision to refer an event for unblinded evaluation." Pharmaceutical statistics vol. (2021). doi: https://doi.org/10.1002/pst.2175
Waterhouse B, Hartford A, Mukhopadhyay S, Ferguson R, Hendrickson BA. Using the Bayesian detection of potential risk using inference on blinded safety data (BDRIBS) method to support the decision to refer an event for unblinded evaluation. Pharm Stat. 2021 Nov 02; doi: 10.1002/pst.2175. Epub 2021 Nov 02. PMID: 34725911.
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Pharm Stat. 2021 Nov 02; doi: 10.1002/pst.2175. Epub 2021 Nov 02.

Using the Bayesian detection of potential risk using inference on blinded safety data (BDRIBS) method to support the decision to refer an event for unblinded evaluation.

Pharmaceutical statistics

Brian Waterhouse, Alan Hartford, Saurabh Mukhopadhyay, Ryan Ferguson, Barbara A Hendrickson

Affiliations

  1. Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, Pennsylvania, USA.
  2. Statistical & Quantitative Sciences, Takeda Pharmaceutical Co. Limited, Cambridge, Massachusetts, USA.
  3. Statistical Innovation, Data and Statistical Sciences, AbbVie Inc., North Chicago, Illinois, USA.
  4. Process Data Analytics, AbbVie, North Chicago, Illinois.
  5. Pharmacovigilance and Patient Safety, AbbVie, North Chicago, Illinois.

PMID: 34725911 DOI: 10.1002/pst.2175

Abstract

In the Sponsor Responsibilities-Safety Reporting Requirements and Safety Assessment for IND and Bioavailability/Bioequivalence Studies: Draft Guidance for Industry (June 2021) the Food and Drug Administration recommends that sponsors develop a Safety Surveillance Plan as a key element of a systematic approach to safety surveillance and describes two possible approaches to assess the aggregate safety data. One approach regularly analyzes unblinded serious adverse events (SAEs) by treatment group. The alternative approach prespecifies estimated background rates for anticipated SAEs in the study population (e.g., myocardial infarctions in an older adult population). If the event rate in the blinded data from the study population exceeds a "trigger rate," then an unblinded analysis by treatment group is conducted. The Bayesian detection of potential risk using inference on blinded safety data (BDRIBS) method has been previously described and offers a quantitative approach for assessing blinded events. In this article we provide a procedural workflow for blinded review of safety data that is consistent with the unblinding "trigger approach" for aggregate safety review. In addition, this publication contextualizes the use of BDRIBS within the broader safety surveillance framework, extends the method to allow for multiple studies, and offers examples of its use in various settings via an R-Shiny application that allows for dynamic visualization and assessment.

© 2021 John Wiley & Sons Ltd.

Keywords: BDRIBS; Bayesian inference; adverse events; blinded monitoring of safety data; clinical trial; safety; signal detection

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