Join Elaine Eisenbeisz as she provides an overview of multiple comparisons and why they can be a problem. She will explain the differences between family wise error rate (FWER) and false discovery rate (FDR). Elaine will also present many options for adjusting statistical tests and explain why pre-planning the corrections, if any, for your study is paramount to a robust research study.
Webinar Includes : All the training handouts , certificate ,Q/A and 90 mins Live Webinar
Why Should You Attend
A research rarely involves just one single statistical test. And multiple testing can result in statistically significant findings just by chance. After all, with the typical Type I error rate of 5% used in most tests, we are allowing ourselves to “get lucky” 1 in 20 times.
There are a number of ways to control for this chance significance. And as with most things statistical, determining a viable adjustment to control for the chance significance depends on what you are doing. Some approaches are good. Some are not so good. And, sometimes an adjustment isn’t even necessary.
- Understanding of Type I and Type II error
- When an adjustment for inflated Type I error is appropriate
- When an adjustment for inflated Type I error is not necessary
- Common procedures to control for Type 1 error
- Difference between FWER and FDR
- Overview of FDA Guidance on Multiplicity
Who Will Benefit
- Trial Sponsors
- 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