Multivariable modeling with cubic regression splines: A principled approach

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.


Issue Date:
2007
Publication Type:
Journal Article
DOI and Other Identifiers:
st0120 (Other)
PURL Identifier:
http://purl.umn.edu/119254
Published in:
Stata Journal, Volume 07, Number 1
Page range:
45-70
Total Pages:
26

Record appears in:



 Record created 2017-04-01, last modified 2017-08-26

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)