Power Analysis for Sample Size Calculations

Why Should You Attend:

The power of your study is the probability that you will find a statistically significant difference or relationship (an “effect”) if that difference or relationship (effect) truly exists in the population.

A study with too small of a sample size is under-powered. This means that even if the effect you are testing for truly exists, you won’t achieve statistical significance. You will waste time by collecting a sample that is too small to properly power a study. Why perform a research if you can’t see significance for your desired effect?

A study with too large of a sample is over-powered. This means that you’ve collected such a large sample that you will see significance even on very small effects. However, the costs of subject recruitment, data collection, and follow-up (if needed) are quite large. Recruiting more subjects than needed unnecessarily inflates the temporal and monetary costs.

Questions related to the feasibility of a study can be answered by power analysis:

• How large of a sample will I need to collect in order to see a significant effect?
• How many subjects will I need if I test an effect that is a bit larger? a bit smaller?

Answers to questions like these will give you an idea if your study is indeed “do-able.”

Areas Covered in the Webinar:

• The usefulness of power analysis
• Overview of power analysis theory and concepts
• Effect size
• Examples of sample size calculations using G*Power software
• Examples of sample size calculations using simulation

Who Will Benefit:

• Trial Sponsors
• Physicians
• Clinical Investigator
• Clinical Research Associates
• Clinical Project Managers/Leaders
• Regulatory Professionals who use statistical concepts/terminology in reporting
• Medical Writers who need to interpret statistical reports
• IRB review board members
• DSMB members

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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