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Multivariate adaptive regression splines: a powerful method for detecting disease–risk relationship differences among subgroups
Author(s) -
York Timothy P.,
Eaves Lindon J.,
van den Oord Edwin J. C. G.
Publication year - 2006
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/sim.2292
Subject(s) - multivariate statistics , multivariate adaptive regression splines , regression , statistics , regression analysis , multivariate analysis , disease , econometrics , computer science , mathematics , nonparametric regression , medicine
In a wide variety of medical research scenarios one is interested in the question whether regression curves differ for subgroups in the sample. Examples are gender differences in the effect of drug treatment or the study of genotype–environment interactions. To address this question exploratory techniques are often required because detailed knowledge concerning the shape of the regression curves and how that shape differs across subgroups is lacking. In this article we explored the power of two such exploratory techniques: multivariate adaptive regression splines (MARS) and least squares curve fitting using polynomials. For this purpose simulations were performed using linear, logistic, and complex non‐linear curves. The power obtained from MARS was on average 1.4 times higher than with polynomials. It was shown that power was higher even if the regression curve was linear, that gains increased with the complexity of the curve, and that for highly non‐linear curves model‐free methods such as MARS might be the only alternative. Copyright © 2005 John Wiley & Sons, Ltd.