Course curriculum

This course provides a practical introduction to pooled analyses of randomized controlled trial (RCT) data, focusing on essential methodologies for clinical trial statisticians. Discover how pooling data enhances the ability to assess efficacy and safety, increases statistical power to detect rare events (e.g., adverse effects), and improves the identification of subgroup effects. Through real-world case studies, you’ll explore strategies for addressing differences between trials and generating more robust clinical insights.

    1. When and Why Are Pooled Analyses Initiated?

    2. What Do People Typically Do?

    1. What Is a Naive Pooled Analysis?

    2. Risks of Naive Pooling

    3. Introduction to Heterogeneity

    4. Why Heterogeneity Matters

    5. A Better Way Forward

    1. Estimating Personalized Treatment Effects

    1. Regulatory Considerations

    1. Continuing Education and Resources

About this course

  • €500,00
  • 10 lessons

Instructors

Tim Friede

Professor

Tim Friede is Professor of Biostatistics at the University Medical Center Göttingen, where he leads the Department of Medical Statistics. With experience in academia and industry, he specializes in clinical trial methodology, adaptive designs, and evidence synthesis. Previously, he held roles at Lancaster University, Novartis, and Warwick Medical School. He is an expert in biometry and a member of the Sino-German Institute for Social Computing and the Campus Institute Data Science. Tim is dedicated to advancing statistical methods for clinical research.

Thomas Debray

Founder, Smart Data Analysis and Statistics

Dr. Thomas Debray is a leading expert in precision medicine and real-world evidence, with over a decade of experience in biostatistics. He has held research positions at Utrecht University, University College London, and the University of Oxford and is a guest scientist at University Medical Center Göttingen. As the founder of SDAS, he collaborates with pharma and biotech companies to advance clinical trial design, predictive modeling, and evidence-based decision-making.

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