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Choice of scale for cubic smoothing spline models in medical applications
Author(s) -
Royston Patrick
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(20000515)19:9<1191::aid-sim460>3.0.co;2-1
Subject(s) - covariate , smoothing spline , smoothing , computer science , spline (mechanical) , logarithm , transformation (genetics) , parametric statistics , scale (ratio) , mathematics , regression analysis , algorithm , econometrics , mathematical optimization , statistics , machine learning , spline interpolation , mathematical analysis , biochemistry , chemistry , physics , structural engineering , quantum mechanics , engineering , bilinear interpolation , gene
The determination of the functional form of the relationship between an outcome variable and one or more continuous covariates is an important aspect of the modelling of medical data. For correct interpretation of the data it is essential that the functional form be specified at least approximately correctly. I show that for given model complexity, logarithmic transformation of a covariate can greatly improve the fit of one of the most useful and convenient non‐parametric regression models, the cubic smoothing spline. A mathematical rationale for the idea is given. I propose a diagnostic for deciding initially whether a log transformation is needed. The method is illustrated using several medical data sets. No special software other than that used for fitting the spline models is needed. Copyright © 2000 John Wiley & Sons, Ltd.