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Apr 01, 2022 10:00 AM - Apr 01, 2022 2:00 PM

(US Eastern Standard Time)

Practical Bayesian Approaches in Healthcare: Industry and Regulatory Perspectives


*Short Courses require an additional registration fee. You do not need to be registered for the forum to attend*

This Short Course will be offering in a virtual format only

In recent years, Bayesian methods have generated extensive discussion in clinical trials. A Bayesian approach provides the formal framework to incorporate external information into the statistical analysis of a clinical trial. It helps to strategically borrow information across different, potentially heterogeneous, groups such as between adults and pediatrics, disease subgroups, regions, etc. This is particularly useful in planning and executing successful global clinical trials. The course will cover some modern applications of Bayesian methods in healthcare with an emphasis on practical and regulatory aspects. Along with methods, the course will demonstrate applications in real-life clinical trials and discuss the impact of utilizing these approaches in decision-making.

This course is beneficial for statistical researchers working in the pharmaceutical industry, academic institutions, or regulatory agencies, or statistics students. The course is oriented toward practitioners who are interested in (i) using Bayesian methods in medical product development; (ii) understanding the regulatory perspectives; and (iii) learning effective ways to communicate with nonstatistical stakeholders.

Learning objectives

At the conclusion of this session, participants should be able to:
  • Discuss utilizing Bayesian methods to support decision making in drug development and clinical trial design and analysis
  • Use available open-source software’s for implementing Bayesian methods in real-life clinical trial
  • Explain the benefits and risks of Bayesian approaches
  • Understand the regulatory landscape while using Bayesian methods in medical product development
  • Assess the findings of Bayesian analyses and communicate these findings with non-statisticians

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