Workshop on Regression Modelling Strategies: Frank Harrell, Jr.

All standard regression models have assumptions that must be verified for the model to have power to test hypotheses and to make accurate predictions. This course will emphasize methods for assessing and satisfying the linearity and additivity assumptions. Practical but powerful tools are presented for validating model assumptions and presenting model results. This course covers methods for: estimating the shape of the relationship between predictors and response by augmenting the design matrix using restricted cubic splines; data reduction and model validation (bootstrap and cross–validation) and topics such as modeling interaction surfaces, multiple imputation, variable selection, outliers, collinearity, and shrinkage.

Biography:

Professor Frank Harrell, Jr., received his Ph.D. in Biostatistics from the University of North Carolina in 1979. Since 2003 he has been Professor of Biostatistics, Vanderbilt University School of Medicine, and was the founding department chair from 2003 - 2017. He is the Expert Statistical Advisor for the Office of Biostatistics for the Center for Drug Evaluation and Research, a division in the U.S. Food and Drug Administration. He is an Associate Editor of Statistics in Medicine, a member of the Scientific Advisory Board for Science Translational Medicine, a member of the Faculty of 1000 Medicine, and a member of the policy advisory board for the Journal of Clinical Epidemiology. 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. He was the 2017 Visionary Speaker at the Clinical Studies Coordinating Center, University of North Carolina Department of Biostatistics, Chapel Hill. He is the author of the book Regression Modeling Strategies, Second Edition (Springer, 2015). His research interests include the 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.