R help - Sep 2006 - Course***Dr Frank Harrell's Regression Modeling Strategies in R/Splus course *** September 2006 near you (San Francisco, Washington DC, Atlanta)

Anyone from Chicago area interested in this course? Please email XLSolutions so
they can schedule it in Chicago.
We ran out of travel budget in my company  :(


Date: Wed, 2 Aug 2006 13:20:23 -0700From: elvis@xlsolutions-corp.comSubject: [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
XLSolutions Corporation (www.xlsolutions-corp.com) is very proud to announce Dr
Frank Harrell's Regression Modeling Strategies in R/Splus this September
2006. http://xlsolutions-corp.com/Rstats2.htm
 
*** 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
 
http://xlsolutions-corp.com/Rstats2.htm
 
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.
 
Course Outline: http://xlsolutions-corp.com/Rstats2.htm
 
- 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.
 
Email us for group discounts.Email Sue Turner: sue@xlsolutions-corp.comPhone:
206-686-1578Visit us: http://xlsolutions-corp.com/Rstats2.htmPlease let us know
if you and your colleagues are interested in thisclass to take advantage of
group discount. Register now to secure yourseat! Cheers,Elvis Miller, PhDManager
Training.XLSolutions Corporation206 686
1578www.xlsolutions-corp.comelvis@xlsolutions-
 
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