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Choosing Between Alternative Models

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Clinical Prediction Models

Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

Any scientific model will have to make simplifying assumptions about reality. Nevertheless, statistical models are important tools to learn from patterns in underlying data. A good model can be used to make accurate predictions for future subjects. We discuss some general issues in choosing a type of model in a prediction context, with illustration in a case study on modeling age–outcome relations in medicine. We also summarize results from some empirical comparisons of alternative models, including classical regression and modern methods related to machine learning approaches.

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Correspondence to Ewout W. Steyerberg PhD .

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Steyerberg, E.W. (2019). Choosing Between Alternative Models. In: Clinical Prediction Models. Statistics for Biology and Health. Springer, Cham. https://doi.org/10.1007/978-3-030-16399-0_6

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