SORA-TABA-DLSPH Workshop 2019

SORA-TABA  Workshop & DLSPH Biostatistics Research Day

Date: May 3, 2019
Time: 8:00 am to 4:00 pm
Location: Jackman Law Building, University of Toronto, 78 Queen’s Park, Toronto
Theme: Regression Modelling Strategies
Instructor: Frank Harrell, Vanderbilt University

The registration fee includes lunch and coffee breaks.

The Division of Biostatistics at the Dalla Lana School of Public Health is pleased to host the SORA-TABA Workshop & DLSPH Biostatistics Research Day. The event brings together regional and local statistical communities who are interested in biostatistics, financial statistics and other applied areas of statistics. Please join us in making this event a great success!

In addition to the lecture presentation, the workshop will include poster presentations by participants; students and post-docs are particularly encouraged to present their research or practicum work, and three poster awards will be given at the closing ceremony. There will also be career advice mentors in attendance who will be available to provide career-building advice, especially for graduate students.

Please note that due to overwhelming demand, registration is not available on site and ALL registration must be made before April 29th. Space is limited and registration will be first come first served.

Regression Modelling Strategies

Instructor: Frank Harrell, Vanderbilt University
Frank Harrell

Regression models are frequently used to develop diagnostic, prognostic, and health resource utilization models in clinical, health services, outcomes, pharmacoeconomic, and epidemiologic research, and in a multitude of non-health-related areas. Regression models are also used to adjust for patient heterogeneity in randomized clinical trials, to obtain tests that are more powerful and valid than unadjusted treatment comparisons. Models must be flexible enough to fit nonlinear and non-additive relationships, but unless the sample size is enormous, the approach to modelling must avoid common problems with data mining or data dredging that result in overfitting and a failure of the predictive model to validate on new subjects. All standard regression models have assumptions that must be verified for the model to have power to test hypotheses and for it to be able to predict accurately. Of the principal assumptions (linearity, additivity, distributional), this short course will emphasize methods for assessing and satisfying the first two. Practical but powerful tools are presented for validating model assumptions and presenting model results. This course provides methods for estimating the shape of the relationship between predictors and response.

The first part of the course presents the following elements of multivariable predictive modeling for a single response variable: using regression splines to relax linearity assumptions, perils of variable selection and overfitting, where to spend degrees of freedom, shrinkage, imputation of missing data, data reduction, and interaction surfaces. Then a default overall modeling strategy will be described, with an eye towards “safe data mining”. This is followed by methods for graphically understanding models (e.g., using nomograms) and using re-sampling to estimate a model’s likely performance on new data.

Participants should have a good working knowledge of multiple regression. The following articles might be read in advance: Harrell, Lee, Mark: Stat in Med 15:361-387, 1996. Spanos, Harrell, Durack: JAMA 262:2700-2707, 1989. See http://fharrell.com/links for more background information and resources.

About the Instructor

  • Professor and founding Chair of the Department of Biostatistics, Vanderbilt University School of Medicine, Nashville TN USA
  • PhD in Biostatistics from U. North Carolina
  • Extensive work in biomedical and pharmaceutical research
  • ASA Fellow and winner of the ASA WJ Dixon Award for Excellence in Statistical Consulting in 2014
  • Publications
  • Active on stats.stackexchange.com – see my posts here
  • Written several R packages including Hmisc and rms
  • Used R intensively since 1999 and am a member of the R Foundation
  • Author of Regression Modeling Strategies, 2nd Edition
  • Statistical knowledge outside the areas of regression modeling strategies and Bayes is in BBR
  • Expert Statistical Advisor to the Office of Biostatistics, Center for Drug Evaluation and Research, FDA

Workshop Committee Members

  • Wendy Lou, University of Toronto, DLSPH
  • Tony Panzarella, University of Toronto, DLSPH
  • Ryan Rosner, University of Toronto, DLSPH
  • Teresa To, Research Institute of the Hospital for Sick Children (SickKids)
  • Fernando Camacho, Damos Inc, University of Waterloo, ASA
  • Janet McDougall. McDougall Scientific, ASA
  • Hugh McCague, York University, SORA
  • Hanna Jankowski, York University, SORA
  • Peter Macdonald, McMaster University, SORA
  • Michael Rotondi, York University, SORA
  • Ruth Croxford, Institute for Clinical Evaluation Sciences, TABA
  • Marguerite Ennis, Applied Statistician, TABA

If you have any question regarding the workshop, please send your inquiry to Ryan Rosner at biostat.dlsph@utoronto.ca.