Tests of Equivalence and Non-Inferiority

Why Should You Attend:

Although the statistics are straightforward and use familiar concepts, there are important differences in the way that these tests are designed and reported. Such tests can be divided into equivalence tests, which are performed to demonstrate that the new treatment is neither better nor worse than the active control, and non-inferiority tests, which are used to demonstrate that the new treatment is not less effective than the active control.

Traditionally, much of medical research has involved finding differences between newer treatments and the previous standard of care, with the hope of showing that the newer treatment is better. Medical statistics has reflected this, with a focus on demonstrating a difference by rejecting the ‘null hypothesis’ that both treatments are similar (so-called ‘superiority studies’). Increasingly, however, new treatments are being developed that may not be better in clinical practice, but which offer advantages in terms of cost, ease of use, or adverse effects.

Learning Objectives:

• Understand why traditional superiority trials cannot demonstrate equivalence or non-inferiority
• Terminology of equivalency and non-inferiority trials
• Describe how equivalency and non-inferiority trials work
• Determine the sample size needed for a equivalency and non-inferiority trial.
• Perform calculations to determine equivalency and non-inferiority

Areas Covered in the Webinar:

• Quick review of null hypothesis statistical testing (NHST) for superiority
• When and Why to use tests of equivalence or non-inferiority
• Tests of Equivalence – Two Ways
  • Two one-sided tests (TOST)
  • Confidence Interval Approach
• Tests for Non-Inferiority
• Determine samples sizes for equivalence or non-inferiority trials
• Considerations for the design of equivalence or non-inferiority trials

Who Will Benefit:

• Statisticians
• Investigators
• Physicians
• Clinical Investigators
• Clinical Research Coordinators
• Clinical Research Nurse Coordinators
• Clinical Research Associates/Assistants
• Clinical Project Managers/Leaders
• Study Managers
• Regulatory Professionals who use statistical concepts/terminology in reporting
• Medical Writers and others who need to interpret statistical reports

Elaine Eisenbeisz
Owner, Omega Statistics

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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