Multivariable modeling with cubic regression splines: A principled approach
Patrick Royston
UK Medical Research Council
London, UK
[email protected]
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Willi Sauerbrei
University Medical Center
Freiburg, Germany
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Abstract. Spline functions provide a useful and flexible basis for modeling
relationships with continuous predictors. However, to limit instability and
provide sensible regression models in the multivariable setting, a
principled approach to model selection and function estimation is important.
Here the multivariable fractional polynomials approach to model building is
transferred to regression splines. The essential features are specifying a
maximum acceptable complexity for each continuous function and applying a
closed-test approach to each continuous predictor to simplify the model
where possible. Important adjuncts are an initial choice of scale for
continuous predictors (linear or logarithmic), which often helps one to
generate realistic, parsimonious final models; a goodness-of-fit test for a
parametric function of a predictor; and a preliminary predictor
transformation to improve robustness.
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Patrick Royston, Willi Sauerbrei
View all articles with these keywords:
mvrs, uvrs, splinegen, multivariable analysis, continuous predictor, regression spline, model building, goodness of fit, choice of scale
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