Power Analysis for Sample Size Calculations

Power Analysis for Sample Size Calculations is organized by Webinar Compliance.

Category: Clinical & Laboratory, Food, Drugs & Biologics, Healthcare, Statistical
Scheduled On: January 25 2022 1:00 pm - US/Canada (Eastern Time - ET)

Who Should Attend:
• 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

Description:
In this webinar attendees will learn the statistical power analysis and techniques for determining sample size (a priori techniques) calculation. Also attendees will get work examples in the free to use G*Power software. Some code and demonstrations will be provided for powering studies and performing power analysis simulations in R software.

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.”

Why You Should 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.

Areas Covered in the Session:
• 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