|
Hi everyone,
The Section on Statistical Consulting will be hosting Frank E Harrell Jr, PhD for their November webinar. Dr. Harrell will be presenting on Controlling α vs. Probability of a Decision Error.
Dr. Harrell is a Professor of Biostatistics at Vanderbilt University's School of Medicine. He has devoted his career to the study of patient outcomes in general and specifically to the development of accurate prognostic and diagnostic models and models for many other patient responses, as is reflected in his almost 300 peer-reviewed publications. His book Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (2nd Edition 2015, Springer-Verlag) contains theory, examples, and detailed case studies demonstrating the use of many modern statistical modeling tools. Additionally, Dr. Harrell has done a significant amount of consulting and collaboration on biomedical research projects in both academic medical centers and in industry. To learn more about Dr. Harrell and his work, please read his brief biography below, or visit https://www.fharrell.com/. You can register for the webinar at:
https://www.amstat.org/ASA/Education/Web-Based-Lectures.aspx
The webinar will be held on Friday, November 13th at 12:00 pm Eastern time, and you must register before Thursday, November 14th at noon Eastern time.
Andrea
***
Title
Controlling α vs. Probability of a Decision Error
Abstract
This presentation clarifies what type I assertion probability protects against, by making a clear distinction between how often we assert an effect vs. how often we are wrong about an effect. It is argued that "error" should be struck from the phrase "type I error probability". Frequentist and Bayesian approaches will be briefly contrasted, with an explanation of why it is confusing to mix the two. Terms such as p-values, α, and "false positive" will be attempted to be precisely defined, and subtleties in defining "false positive probability" will be discussed. Then emphasis is placed on multiplicity issues that can occur when analyzing data multiple times, and why such issues do not apply to the Bayesian paradigm. By way of pattern recognition, medical diagnosis, and sequential clinical trial examples it is explained why α loses relevance once data are available.
Bio
Dr. Harrell received his PhD in Biostatistics from UNC in 1979. Since 2003 he has been Professor of Biostatistics, Vanderbilt University School of Medicine, and was the department chairman from 2003-2017. He was Expert Statistical Advisor for the Office of Biostatistics for FDA CDER from 2016-2020. He is Associate Editor of Statistics in Medicine, and a member of the Scientific Advisory Board for Science Translational Medicine. He is a Fellow of the American Statistical Association and winner of the Association's WJ Dixon Award for Excellence in Statistical Consulting for 2014. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, cardiovascular research, technology evaluation, pharmaceutical safety, Bayesian methods, quantifying predictive accuracy, missing data imputation, and statistical graphics and reporting.
|