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Multivariable modeling with cubic regression splines: A principled approach

  • Patrick Royston

    ()

    (Cancer Group, MRC Clinical Trials Unit)

  • Willi Sauerbrei

    (University Medical Center, Freiburg)

Registered author(s):

    Spline functions provide a useful and flexible basis for modeling re- lationships 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 frac- tional 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 pre- dictor 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. Copyright 2007 by StataCorp LP.

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    Article provided by StataCorp LP in its journal Stata Journal.

    Volume (Year): 7 (2007)
    Issue (Month): 1 (February)
    Pages: 45-70

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    Handle: RePEc:tsj:stataj:v:7:y:2007:i:1:p:45-70
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