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

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  • Rhodes, Stephen

    (University Hospitals Cleveland Medical Center)

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.

Suggested Citation

  • Rhodes, Stephen, 2022. "Using restricted cubic splines to assess the calibration of clinical prediction models: Logit transform predicted probabilities first," OSF Preprints 4n86q, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:4n86q
    DOI: 10.31219/osf.io/4n86q
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