What You’ll Learn in This Episode:

✔ The true definitions of interim, final, primary, confirmatory, and updated analyses

✔ Why a “final analysis” doesn’t always mean “last analysis”

✔ How language choice can impact stakeholder trust and regulatory interpretation

✔ The operational vs. statistical meaning of “stopping a study”

✔ When to use which data cuts and how to define your analysis set

✔ The nuances of estimation vs. hypothesis testing after stopping a trial

✔ Why clarity in communication is just as critical as technical accuracy

Why You Should Listen:

Even experienced statisticians often use terms like “final” or “interim” inconsistently, which can cause confusion not just within study teams, but also with regulators, clinicians, and the public.

Kaspar provides much-needed clarity on how we as statisticians can:

• Use more precise terminology when describing study analyses,
• Align our language with stakeholders,
• Avoid miscommunication that can erode public and regulatory trust, and
• Educate others on the importance of estimating vs. testing.

Resources & Links:

🔗 LinkedIn article by Kaspar Rufibach on clinical trial

🔗 Preprint Paper (with industry and regulatory co-authors)

🔗 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.

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