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

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.

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.

Areas Covered in the Webinar:

• 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

This Training will Focus On:

• 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

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

Elaine Eisenbeisz
Owner, Omega Statistics

Elaine Eisenbeisz is a Statistician with over 30 years of experience in pharmaceutical and biotechnology research and development. She is the founder of Omega Statistics (est. 2006), a consulting practice guided by the principle of “Elegant Solutions, Effectively Applied.”

Elaine has directed the statistical planning, analytical execution, and regulatory reporting for clinical trials and research, pharmacovigilance activities, real-world evidence studies, and quality systems operating within complex regulatory environments aligned with ICH and FDA guidance. Her work focuses on bridging statistical methodology and regulatory judgment — ensuring that uncertainty, risk tolerance, and evidentiary standards are clearly understood and defensibly applied. She is a recognized educator and advisor who equips QA and Regulatory leaders to interpret statistical evidence with clarity, rigor, and strategic insight.

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