Statistical Elements of Sample Size Calculations for Non-Clinical Verification and Validation Studies
Product Id : FDB3134
Category : Clinical & Laboratory, Food, Drugs & Biologics, Medical Devices, Quality, Statistical
Presenter : Elaine Eisenbeisz
Scheduled On : February 10 2022 1:00 pm
US/Canada (Eastern Time - ET)
Duration : 90 Minutes
This webinar provides the logic and processes for determining samples sizes for common tests used in verification or validation of processes. The focus of this webinar is on providing the information needed for attendees to know the appropriate measures and formulas to use for the determining sample size and providing justification for the planned sample sizes.
Why You Should Attend:
Verification and validation studies of design-outputs and/or manufacturing processes are required in many manufacturing processes. However, it can be difficult to understand the rational for same sizes used in these contexts. This webinar will be useful to those interested in learning how to make and justify the reasoning behind sample size determination.
Learn the theory, terminology, regulatory requirements, best practices, and of course, the steps for calculating sample sizes for process verification and validation.
NOTE: This webinar does not address rationales for sample sizes used in clinical trials.
Learning Objectives:
Understand statistical concepts and terminology related to sample size determination
Use of open-source GPower software to perform sample size calculations
How to write a justification statement for the rationale used to determine sample size.
Areas Covered in the Session :
Regulatory Requirements for sample size in verification and validation
Population vs. Sample, Statistical Theory and Terminology
Confidence Intervals
Statistical Process Control Charts
Process Capability Indices
Confidence/Reliability Calculations
Tests of Significance
Mean Time Between Failure (MTBF) studies
Example of sample size determination
Tips for writing “sample size rationale” statements that are statistically sound.
Who Should Attend:
QA/QC Supervisors
Process Engineers
Manufacturing Engineers
QA/QC Technicians
Manufacturing Technicians
R&D Engineers
Elaine Eisenbeisz
Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.
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 received her Master’s Certification in Applied Statistcs from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.
Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She currently is an investigator on approximately 10 proton therapy clinical trials for Proton Collaborative Group, based in Illinois. She also designs and analyzes studies as a contract statistician for nutriceutical and fitness studies with QPS, a CRO based in Delaware. Elaine has also worked as a contract statistician with numerous private researchers and biotech start-ups as well as with larger companies such as Allergan and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals. Please visit the Omega Statistics website at www.OmegaStatistics.com to learn more about Elaine and Omega Statistics.
