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

• Published in: Pharmaceutical statistics : the journal of the pharmaceutical industry Vol. 21; no. 2; pp. 372 - 385
• Main Authors: Waterhouse, Brian, Hartford, Alan, Mukhopadhyay, Saurabh, Ferguson, Ryan, Hendrickson, Barbara A.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01-03-2022; Wiley Subscription Services, Inc
• Subjects: adverse events; Aged; Bayes Theorem; Bayesian inference; BDRIBS; blinded monitoring of safety data; clinical trial; Humans; Research Design; safety; signal detection; Surveillance; United States; United States Food and Drug Administration; United States; clinical trial; signal detection; blinded monitoring of safety data; safety; adverse events; Bayesian inference; BDRIBS
• ISSN: 1539-1604; 1539-1612
• DOI: 10.1002/pst.2175

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Reporting Requirements and Safety Assessment for IND and Bioavailability/Bioequivalence Studies: Draft Guidance for Industry (June 2021) the Food and Drug Administration recommends that sponsors develop a Safety Surveillance Plan as a key element of a systematic approach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.