000119254 001__ 119254
000119254 005__ 20181128180053.0
000119254 0247_ $$2Other$$ast0120
000119254 037__ $$a199-2016-2454
000119254 037__ $$a199-2016-2896
000119254 041__ $$aen_US
000119254 245__ $$aMultivariable modeling with cubic regression splines: A principled approach
000119254 260__ $$c2007
000119254 269__ $$a2007
000119254 270__ $$mpatrick.royston@ctu.mrc.ac.uk$$pRoyston,   Patrick
000119254 300__ $$a26
000119254 336__ $$aJournal Article
000119254 520__ $$aSpline functions provide a useful and flexible basis for modeling relationships with continuous predictors. However, to limit instability and provide sensible regression models in the multivariable setting, a principled approach to model selection and function estimation is important. Here the multivariable fractional polynomials approach to model building is transferred to regression splines. The essential features are specifying a maximum acceptable complexity for each continuous function and applying a closed-test approach to each continuous predictor to simplify the model where possible. Important adjuncts are an initial choice of scale for continuous predictors (linear or logarithmic), which often helps one to generate realistic, parsimonious final models; a goodness-of-fit test for a parametric function of a predictor; and a preliminary predictor transformation to improve robustness.
000119254 542__ $$fLicense granted by Lisa Gilmore (lgilmore@stata.com) on 2011-12-27T18:38:37Z (GMT):

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000119254 650__ $$aResearch Methods/ Statistical Methods
000119254 6531_ $$amvrs
000119254 6531_ $$auvrs
000119254 6531_ $$asplinegen
000119254 6531_ $$amultivariable analysis
000119254 6531_ $$acontinuous predictor
000119254 6531_ $$aregression spline
000119254 6531_ $$amodel building
000119254 6531_ $$agoodness of fit
000119254 6531_ $$achoice of scale
000119254 700__ $$aRoyston, Patrick
000119254 700__ $$aSauerbrei, Willi
000119254 773__ $$d1st Quarter 2007$$j 07$$k 1$$o70$$q45$$tStata Journal
000119254 8564_ $$s235409$$uhttp://ageconsearch.umn.edu/record/119254/files/sjart_st0020.pdf
000119254 887__ $$ahttp://purl.umn.edu/119254
000119254 909CO $$ooai:ageconsearch.umn.edu:119254$$pGLOBAL_SET
000119254 912__ $$nSubmitted by Lisa Gilmore (lgilmore@stata.com) on 2011-12-27T18:41:06Z
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sjart_st0020.pdf: 235409 bytes, checksum: a708bf66491f3dd892af32dd43e9438c (MD5)
000119254 912__ $$nMade available in DSpace on 2011-12-27T18:41:09Z (GMT). No. of bitstreams: 1
sjart_st0020.pdf: 235409 bytes, checksum: a708bf66491f3dd892af32dd43e9438c (MD5)
  Previous issue date: 2007
000119254 982__ $$gStata Journal>Volume 7, Number 1, 1st Quarter 2007
000119254 980__ $$a199