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Activity Number:
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315
- Innovative Bayesian Approaches in Clinical Trials and Practical Considerations
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Type:
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Invited
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Date/Time:
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Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #300341
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Title:
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Bayesian Framework for Pediatric Drug Development
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Author(s):
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Amarjot Kaur* and Mandy Jin and Qing Li
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Companies:
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Merck & Co. and Merck & Co., Inc. and Merck Research Labs
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Keywords:
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Bayesian framework;
Hierarchical Modeling;
clinical trials;
historical data
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Abstract:
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The efficacy and safety evaluation in pediatric subjects is an important component of drug development and is generally mandated by the regulatory agencies. Pediatric trials are usually conducted after the efficacy and safety has been established in adult population and therefore has access to historical information from adult trials. Many disease areas are presented with inherent difficulties in conducting large pediatrics trials making such development highly challenging. Bayesian framework can provide analytical avenues to effectively utilize historical information in design and analysis and enhance efficiencies in terms of sample size and power. In this presentation we will discuss some practical scenarios where Bayesian framework can provide a reasonable path forward to demonstrate efficacy requirements for pediatric program. There are different ways to incorporate historical information and we will focus on Bayesian hierarchical model for continuous endpoints (Schoenfeld et al., 2009) and provide the extension of this framework for binary endpoints. Sensitivity analysis for the underlying assumptions will also be discus
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Authors who are presenting talks have a * after their name.
