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.
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.
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
LEARNING OBJECTIVES
- 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
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.
- 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
- 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
- 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
