• Martin Gregory, Hanno Ulmer, Karl P Pfeiffer, Stefan Lang, and Alexander M Strasak.
• SAS Institute Inc., Heidelberg, Germany.
• Comput Methods Programs Biomed. 2008 Oct 1; 92 (1): 109-14.

AbstractIn clinical and epidemiologic research to investigate dose-response associations, non-parametric spline regression has long been proposed as a powerful alternative to conventional parametric regression approaches, since no underlying assumptions of linearity have to be fulfilled. For logistic spline models, however, to date, little standard statistical software is available to estimate any measure of risk, typically of interest when quantifying the effects of one or more continuous explanatory variable(s) on a binary disease outcome. In the present paper, we propose a set of SAS macros which perform non-parametric logistic regression analysis with B-spline expansions of an arbitrary number of continuous covariates, estimating adjusted odds ratios with respective confidence intervals for any given value with respect to a supplied reference value. Our SAS codes further allow to graphically visualize the shape of the association, retaining the exposure variable under consideration in its initial, continuous form while concurrently adjusting for multiple confounding factors. The macros are easily to use and can be implemented quickly by the clinical or epidemiological researcher to flexibly investigate any dose-response association of continuous exposures with the risk of binary disease outcomes. We illustrate the application of our SAS codes by investigating the effect of body-mass index on risk of cancer incidence in a large, population-based male cohort.

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