From P-Values to Evidence: Interpreting Statistical Results in Regulatory Decision-Making

Statistical significance alone is no longer sufficient to support regulatory claims. Modern regulatory review requires careful interpretation of effect size, confidence intervals, robustness, and clinical relevance. This webinar provides a technically grounded, example-driven approach to interpreting p-values and statistical results in regulatory submissions.

For decades, statistical interpretation in regulatory submissions has centered on whether a p-value crosses a fixed threshold. However, regulatory agencies increasingly emphasize effect magnitude, precision, clinical relevance, and methodological robustness when evaluating evidence. Over-reliance on p-values can lead to weak justifications, regulatory questions, or delayed approvals.

This technically grounded session examines what p-values measure mathematically, why they are limited when used alone, and how confidence intervals, effect sizes, and sensitivity analyses strengthen evidentiary interpretation. Through worked examples and step-by-step analysis, participants will learn how to critically evaluate statistical findings to ensure they are defensible in regulatory decision-making.
 

• P-value interpretation
• Hypothesis testing in regulatory submissions
• Statistical significance vs clinical significance
• Confidence intervals in regulatory decision-making
• Effect size interpretation
• Regulatory statistical review
• ICH E9 R1 estimand guidance
• FDA statistical expectations
• Non-inferiority margin justification
• Multiplicity in clinical trials
• Sensitivity analysis in submissions
• Regulatory defensibility of statistical evidence
• Benefit–risk statistical evaluation
• Type I and Type II error in regulatory context
• Clinical trial statistical interpretation
• Pharmacovigilance statistical analysis
• QA statistical review checklist
• Regulatory submission statistical standards
• Precision and uncertainty in clinical research
• Statistical robustness in regulatory decisions

• Logic of hypothesis testing
• What a p-value measures — and its limitations
• Relationship between p-values and confidence intervals
• Effect size interpretation in regulatory context
• Precision and uncertainty assessment
• Large-sample vs small-sample interpretation challenges
• Sensitivity analyses and robustness evaluation
• Clinical vs statistical vs regulatory significance
• Practical checklist for reviewing statistical evidence

• Regulatory Affairs Directors and Managers
• Quality Assurance Directors and Managers
• Compliance Officers
• Clinical Development Leaders
• Medical Affairs Professionals
• Biostatistics and Data Science Leaders
• Regulatory Submission Strategists
• Pharmacovigilance Managers

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California.
Elaine earned her B.S. in Statistics at UC Riverside and received her Master’s Certification in Applied Statistics from Texas A&M.
Elaine is a member in good standing with the American Statistical Association and a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.

Elaine has designed the methodology and analyzes data for numerous studies in the clinical, biotech, and health care fields. Elaine has also works as a contract statistician with private researchers and biotech start-ups as well as with larger companies such as Allergan, Nutrisystem and Rio Tinto Minerals. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals.