TY - JOUR
ID - st0120
A1 - Royston, P.
A1 - Sauerbrei, W.
TI - Multivariable modeling with cubic regression splines: A principled approach
JF - Stata Journal
PB - Stata Press
CY - College Station, TX
Y1 - 2007
VL - 7
IS - 1
SP - 45
EP - 70
KW - mvrs
KW - uvrs
KW - splinegen
KW - multivariable analysis
KW - continuous predictor
KW - regression spline
KW - model building
KW - goodness of fit
KW - choice of scale
UR - http://www.stata-journal.com/article.html?article=st0120
L1 - http://www.stata-journal.com/sjpdf.html?article=st0120
AB - 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.
ER -