Using restricted cubic splines to assess the calibration of clinical prediction models: Logit transform predicted probabilities first

Stephen Rhodes
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Stephen Rhodes: University Hospitals Cleveland Medical Center

No 4n86q, OSF Preprints from Center for Open Science

Abstract: Non-linear calibration curves allow researchers to assess the relationship between the predicted and observed probability of an outcome. This can be achieved via the use of a restricted cubic spline in a logistic regression model relating the predicted probabilities to the observed binary outcome. The present simulation study shows that using the predicted probabilities directly (the default in R functions available) leads to incorrect calibration curves that suggest miscalibration of correctly specified models. Further, the degree of the suggested miscalibration depends on the degree of non-linearity or interaction present. Better performance is achieved by first logit transforming predicted probabilities.

Date: 2022-11-04
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:4n86q

DOI: 10.31219/osf.io/4n86q

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