Abstract
If we develop a statistical model with the main aim of outcome prediction, we are primarily interested in the validity of the predictions for new subjects, outside the sample under study. A key threat to validity is overfitting: the data under study are well described, but predictions are not valid for new subjects. Overfitting causes optimism about a model’s performance in new subjects. After introducing overfitting and optimism, we illustrate overfitting with a simple example of comparisons of mortality figures by hospital. We find that we would exaggerate any true patterns of differences between centers, if we would use the observed average outcomes per center as predictions of mortality. Bootstrap resampling is presented as a central technique to correct overfitting and quantify optimism in model performance.
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Steyerberg, E.W. (2019). Overfitting and Optimism in Prediction Models. In: Clinical Prediction Models. Statistics for Biology and Health. Springer, Cham. https://doi.org/10.1007/978-3-030-16399-0_5
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DOI: https://doi.org/10.1007/978-3-030-16399-0_5
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16398-3
Online ISBN: 978-3-030-16399-0
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