Abstract
In fitting regression models data analysts are often faced with many predictor variables which may influence the outcome. Several strategies for selection of variables to identify a subset of ‘important’ predictors are available for many years. A further issue to model building is how to deal with non-linearity in the relationship between outcome and a continuous predictor. Traditionally, for such predictors either a linear functional relationship or a step function after grouping is assumed. However, the assumption of linearity may be incorrect, leading to a misspecified final model. For multivariable model building a systematic approach to investigate possible non-linear functional relationships based on fractional polynomials and the combination with backward elimination was proposed recently. So far a program was only available in Stata, certainly preventing a more general application of this useful procedure. The approach will be introduced, advantages will be shown in two examples, a new approach to present FP functions will be illustrated and a macro in SAS will be shortly introduced. Differences to Stata and R programs are noted.
Published Version
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