Date: Wed, 2 Aug 2006 13:20:23 -0700
From: elvis@xlsolutions-corp.com
Subject: [S] Course***Dr Frank Harrell's Regression Modeling Strategies in R/Splus course *** September 2006 near you (San Francisco, Washington DC, Atlanta)
To: s-news@wubios.wustl.edu
*** San Francisco, CA / August 31st - September 1st, 2006 ***
*** Atlanta, GA / September 18th - 19th, 2006 ***
*** Washington, DC / September 28th - 29th, 2006 ***
Please ask for group discount and reserve your seat Now - Earlybird Rates.
Note that payment is due after the class! Email Sue Turner:
sue@xlsolutions-corp.com
This two-day course is designed for persons interested in multivariable regression analysis of univariate responses, in developing, validating, and graphically describing multivariable predictive models. 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. 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. Then the freely available S-Plus Design library will be overviewed. Design facilitates most of the steps of the modeling process. Next, statistical methods related to binary logistic models will be covered. Three of the following case studies will be presented: an exploration of voting tendencies over U.S. counties in the 1992 presidential election, an interactive exploration of the survival status of Titanic passengers, an interactive case study in developing a survival time model, and a case study in Cox regression. In the hands-on computer lab students will develop, validate, and graphically describe multivariable regression models themselves. This short course will also survey the advantages of modeling in randomized trials. The methods covered in this course will apply to almost any regression model, including ordinary least squares, logistic regression models, and survival models.
- Planning for Modeling, Covariable Adjustment.
- Notation for Regression Models, Interpreting Model Parameters.
- Relaxing Linearity Assumption for Continuous Predictors; Splines for Estimating Shape of Regression Function and Determining Predictor Transformations, Cubic Spline Functions, Advantages of Splines over Other Methods.
- Tests of Association, Assessment of Model Fit; Regression Assumptions Modeling and Testing Interactions.
- Missing Data; Strategies for Developing Imputation Algorithms , software for Fitting Models and Adjusting Variances for Multiple Imputation.
- Multivariable Modeling Strategy; Pre-Specification of Predictor Complexity ,Variable Selection ,Overfitting and Limits on Number of Predictors, Shrinkage, Data Reduction.
- Resampling, Validating, Describing, and Simplifying the Model; The Bootstrap, Model Validation , Graphically Describing the Fitted Model, Simplifying the Model by Approximating It.
- Design library
- Binary Logistic Regression, Interactive Case Study: Binary Logistic Model for Survival of Titanic Passengers.
- Interactive Case Study: Development of a Long-Term Survival Model for Critically Ill Patients.
- Cox Proportional Hazards Model, Case Study in Cox Regression.
- Case Study using Least Squares Multiple Regression: Voting Patterns in U.S. Counties.