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A Framework for Simple and Efficient Bootstrap Validation in SAS, with Examples

Started ‎03-27-2021 by
Modified ‎05-07-2021 by
Views 6,678
Paper 1034-2021

Isaiah Lankham, University of California Office of the President, Oakland, CA; 

Matthew Slaughter, Kaiser Permanente Center for Health Research, Portland, OR




Validation is essential for assessing a predictive models performance with respect to optimism or overfitting. While traditional sample-splitting techniques (like cross-validation) require us to divide our data between model building and model assessment, bootstrap validation enables us to use the full sample for both. Theres a simple method for efficiently calculating bootstrap-corrected measures of predictive model performance in SAS. While several SAS procedures have options for automatic cross-validation, bootstrap validation requires a more manual process. See examples that focus on logistic regression using the LOGISTIC procedure in SAS/STAT(r), with additional discussion of how these techniques can be extended to other procedures and statistical models.


The running example can be downloaded in Jupyter Notebook and .sas file format from


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Watch A Framework for Simple and Efficient Bootstrap Validation in SAS, with Examples as presented by the authors on the SAS Users YouTube channel.





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I review all of the four examples, try to modify them for Fine-Gray model but not successful.

Can you give an example for Fine-Gray model with variable selection?

Thank you!



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Last update:
‎05-07-2021 01:58 PM
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