🎤  Elaine Eisenbeisz      |  📅  April 18, 2024   |  🕒  11 AM Eastern Time US


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

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

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