Top 5: The analysis of adverse events done right
What You’ll Learn:
✔ Why analyzing adverse events differently from efficacy endpoints creates problems.
✔ How differing follow-up times and censoring bias AE results.
✔ The role of the Aalen–Johansen estimator and why it should be standard practice.
✔ What the SAVVY collaboration achieved by uniting pharma, academia, and regulators.
✔ Real-world examples of how safety analyses can dramatically change the interpretation of treatment risk.
✔ Lessons on collaboration, methodology, and change management in the pharma industry.
Why You Should Listen:
Adverse events are a critical part of any trial, yet they’re often analyzed using simplistic methods that can mislead decision-makers. This episode will help you:
Gain insights you can apply immediately to your own projects to improve the accuracy and credibility of your analyses.
Understand the hidden biases in traditional AE analysis.
Learn how to align safety and efficacy assessments for a fairer benefit–risk evaluation.
Discover the power of collaboration between pharma, academia, and regulators through the SAVVY project.
Links:
🔗 The Effective Statistician Academy – I offer free and premium resources to help you become a more effective statistician.
🔗 Medical Data Leaders Community – Join my network of statisticians and data leaders to enhance your influencing skills.
🔗 My New Book: How to Be an Effective Statistician - Volume 1 – It’s packed with insights to help statisticians, data scientists, and quantitative professionals excel as leaders, collaborators, and change-makers in healthcare and medicine.
🔗 PSI (Statistical Community in Healthcare) – Access webinars, training, and networking opportunities.
Join the Conversation:
Did you find this episode helpful? Share it with your colleagues and let me know your thoughts! Connect with me on LinkedIn and be part of the discussion.
Subscribe & Stay Updated:
Never miss an episode! Subscribe to The Effective Statistician on your favorite podcast platform and continue growing your influence as a statistician.


Comments
New comment