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

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Elaine Eisenbeisz

Speaker: Elaine Eisenbeisz

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Date
08 June, 2026 (Monday)
Time
10:00 AM PDT | 01:00 PM EDT
Duration
90 Minutes

Overview


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.
 

Area Covered

  • 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

Why Should You Attend

Many regulatory professionals assume that achieving p < 0.05 is enough to support a claim. In reality, regulators frequently question whether results are clinically meaningful, precisely estimated, and methodologically robust.
Do you know how to interpret a statistically significant result with a trivial effect size? Can you defend a non-significant result when confidence intervals suggest potential benefit? Are your non-inferiority margins properly justified?
Misinterpreting statistical evidence can weaken submissions and invite regulatory scrutiny. This training equips QA and Regulatory professionals with a structured, technical framework to interpret statistical results beyond simple significance testing — reducing risk and strengthening regulatory defensibility.

Who Will Benefit?

  • 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

Speaker

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.

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