SHORT COURSE 1

TITLE: Medical Product Safety Evaluation: Biological Models & Statistical Methods
INSTRUCTORS: Dr. Jie Chen, Merck Research Laboratories
MODERATOR: Naitee Ting

 

Abstract

This source provides cutting-edge biological models and statistical methods that are tailored to specific objectives and data types for safety analysis and benefit-risk assessment. Some frequently encountered issues and challenges in the design and analysis of safety studies are discussed with illustrative applications and examples.

Specifically, the short course will first give an overview of regulatory requirements on product safety and some general considerations for the design and analysis of safety studies.  Biological models will be introduced with focuses on pharmacokinetic-pharmacodynamic models, predictive toxicology and regulatory framework in predictive toxicology.  Statistical methodologies in benefit-risk assessment will be presented.  Some design and analysis consideration for clinical trials that use pre-identified safety events as a primary endpoint will be discussed.  Statistical methods handling multiplicity, one of the most challenging issues in safety data analysis, will also be presented.  Causal inference methodologies will be introduced for use to establish causal relationship of a treatment with safety outcomes using observation studies and real-world data.  Finally, some recent advances in pharmacovigilance (e.g., meta-analysis, likelihood ratio test, and pharmacoepidemioligc methods) and sequential surveillance methods will be discussed.

Day 1 of the source will cover:

  • A brief history of medical product regulation and relevant regulatory guidelines
  • Biological models and associated statistical methods
  • Design and analysis of clinical trials with safety endpoints
  • Multiplicity issue in safety evaluation

Day 2 of the course will cover:

  • Benefit-risk assessment
  • Statistical methods

In pharmacovigilance

  • Sequential surveillance
  • Benefit-risk analysis

 

Instructor Bio’s

Jie Chen is a Distinguished Scientist in Methodology Research at Merck Research Laboratories, Merck & Co., Inc.  He has nearly 25 years of experience in biopharmaceutical R&D with research interest in the areas of innovative trial design, data analysis, Bayesian methods, multiregional clinical trials, data mining, machine learning methods, and medical product safety evaluation.

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