# Optimism Correction after LASSO in clinical prediction models

**URL:** <https://discourse.datamethods.org/t/optimism-correction-after-lasso-in-clinical-prediction-models/28579>\
**Category:** modeling strategy\
**Tags:** rms, calibration, prediction\
**Created:** [December 31, 2025, 3:12pm UTC](https://discourse.datamethods.org/t/optimism-correction-after-lasso-in-clinical-prediction-models/28579 "2025-12-31T15:12:18Z")\
**Posts on this page:** 1\
**Showing post:** 18

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**Author:** ![f2harrell](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/f2harrell/32/165_2.png) [@f2harrell](https://discourse.datamethods.org/u/f2harrell)\
**Post date:** [January 22, 2026, 2:26pm UTC](https://discourse.datamethods.org/t/optimism-correction-after-lasso-in-clinical-prediction-models/28579/18 "2026-01-22T14:26:19Z")

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Even when methods should theoretically prevent overfitting it’s a good idea to use the Efron-Gong optimism bootstrap to check this, to hedge our bets. On the other hand if you are using a shrinkage method that doesn’t solve the problem it was intended for perhaps a different method should be chosen. Note that the bootstrap doesn’t correct a model; it just tells you how bad the model is.

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