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Bayesian detection of potential risk using inference on blinded safety data.

Saurabh Mukhopadhyay¹, Brian Waterhouse¹, Alan Hartford¹

¹AbbVie Inc., North Chicago, IL, USA.

Pharmaceutical Statistics

|September 1, 2018

Summary

This study introduces a novel Bayesian statistical method for detecting safety signals in blinded clinical trials. It helps assess risks of adverse events of special interest (AESI) before therapy approval.

Keywords:

Bayesian inference blinded monitoring of safety data clinical trial

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Area of Science:

Clinical Trials Methodology
Pharmacovigilance
Biostatistics

Background:

Regulatory bodies increasingly emphasize aggregate safety reviews for new therapies.
Assessing risks from blinded safety data during pre-approval is a significant challenge for sponsors.
Identifying safety signals early is crucial for patient safety.

Purpose of the Study:

To propose a novel quantitative statistical method for monitoring and detecting safety signals in blinded clinical trials.
To address the challenge of risk assessment using blinded safety data during the pre-approval period.
To specifically detect safety signals for adverse events of special interest (AESI) using historical background rates.

Main Methods:

A two-step Bayesian evaluation framework for safety signals.
Includes an initial screening analysis followed by a sensitivity analysis.
Utilizes historical data to establish background rates for AESI.

Main Results:

The proposed Bayesian modeling framework enables inference on relative risk in blinded trials.
The method effectively detects potential safety signals for AESI.
Provides a systematic approach for safety surveillance.

Conclusions:

The novel Bayesian method offers a sound quantitative approach for pre-approval safety surveillance.
Blinded safety teams can utilize this method to assess and escalate potential safety signals for unblinded review.
This facilitates proactive risk management in clinical trial development.