Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small
<br><strong>Objectives<br></strong> When developing a clinical prediction model, penalization techniques are recommended to address overfitting, as they shrink predictor effect estimates toward the null and reduce mean-square prediction error in new individuals. However, shri...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2020
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