2-Hour Virtual Seminar on Power Analysis for Sample Size Calculations

Elaine Eisenbeisz 
Instructor: Elaine Eisenbeisz 
Date: Friday April 16, 2021
Time:

10:00 AM PDT | 01:00 PM EDT

Duration: 2 Hours
Webinar Id: 20031

Price Details

Live Webinar
$190. One Attendee
$390. Unlimited Attendees
Recorded Webinar
$240. One Attendee
$440. Unlimited Attendees
Combo Offers   (Live + Recorded)
$289 $430   One Attendee
$599 $830   Unlimited Attendees

Unlimited Attendees: Any number of participants

Recorded Version: Unlimited viewing for 6 months (Access information will be emailed 24 hours after the completion of live webinar)

Overview:

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 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

Speaker Profile
Elaine Eisenbeisz is a private practice consultant based in Southern California. She has over 20 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.

In addition to her technical expertise, Elaine possesses a talent for conveying statistical concepts and results in a way that people can intuitively understand.

Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation Scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine completed her graduate certification in Applied Statistics with Texas A & M University. Gig ‘em Aggies! Currently, she is finishing her graduate work in Applied Statistics at Rochester Institute of Technology.

Elaine is a member of The American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Elaine is also a member in good standing with the Better Business Bureau.

Current areas of interest include Bayesian inference, simulation and bootstrapping techniques, and predictive modeling.

When she isn’t crunching numbers you can find Elaine digging in her garden, playing her violin, cooking, or playing board games with friends.

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