đźŽ¤ Elaine Eisenbeisz  đź“… April 18, 2024  đź•’ 11 AM Eastern Time US
 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: â€“ How large of a sample will I need to collect in order to see a significant effect? â€“ 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 â€śdoable.â€ť 
^{Why You Should Attend:}^{ }
The power of your study is the probability that you will find a statistically significant difference or relationship (an â€śeffectâ€ť) if that difference or relationship (effect) truly exists in the population.
A study with too small of a sample size is underpowered. This means that even if the effect you are testing for truly exists, you wonâ€™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 canâ€™t see significance for your desired effect?
A study with too large of a sample is overpowered. This means that youâ€™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 followup (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
