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Activity Number:
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487
- Topics in Clinical Trials - II
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #313151
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Title:
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Bayesian Hierarchical Model for Safety Signal Detection in Multiple Clinical Trials
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Author(s):
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Yafei Zhang* and Li-An Lin and William W Wang and Sammy Yuan and Barry Eagel and Hal Li
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Companies:
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Merck and Merck and Merck & Co, Inc and Kite Pharma and Clinical Research and Pharmacovigilance Consultant and Merck & Co., Inc.
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Keywords:
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Bayesian hierarchical model;
meta-analysis;
randomized clinical trial;
drug safety;
pharmacovigilance
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Abstract:
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Clinical safety signal detection is of great importance during drug development and is of critical importance to establish the safety profile of new drugs and biologics during the course of clinical trial. Bayesian hierarchical meta-analysis has proven to be a very effective method to identify potential safety signals by considering the hierarchical structure of clinical safety data from multiple randomized clinical trials conducted under a New Drug Application (NDA) or Biological License Application (BLA). We proposed to extend an existing four-stage Bayesian hierarchical model and considered the exposure adjusted incidence rate assuming the number of adverse events (AEs) follows a Poisson distribution. The proposed model is applied to a case study with three randomized clinical trials in a neuroscience drug program, and examined in three simulation studies motivated by a real world case study. Comparison is made between the proposed method and other existing methods. The simulation results indicate that our proposed model outperforms other two candidate models in terms of power and false discovery rate.
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Authors who are presenting talks have a * after their name.
