Fast FSR variable selection with applications to clinical trials
Related Experiment Video
Updated: Jun 28, 2026

In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Dennis D Boos1, Leonard A Stefanski, Yujun Wu
1Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695-8203, USA. boos@stat.ncsu.edu
Abstract:
A new version of the false selection rate variable selection method of Wu, Boos, and Stefanski (2007, Journal of the American Statistical Association 102, 235-243) is developed that requires no simulation. This version allows the tuning parameter in forward selection to be estimated simply by hand calculation from a summary table of output even for situations where the number of explanatory variables is larger than the sample size. Because of the computational simplicity, the method can be used in permutation tests and inside bagging loops for improved prediction. Illustration is provided in clinical trials for linear regression, logistic regression, and Cox proportional hazards regression.
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