Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs.
Computational Statistics and Data Analysis
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. © 2005 Elsevier B.V. All rights reserved.
|Title:||Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs|
|Keywords:||Fractional polynomials, Function selection, Multivariable model building, Programs|
|UCL classification:||UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Population Health Sciences
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