Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs
Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs
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DOI：
10.1016/j.csda.2005.07.015
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发表时间：
2006-08-01
影响因子：
1.8
通讯作者：
Royston, P.
中科院分区：
文献类型：
--
作者：
Sauerbrei, W.;Meier-Hirmer, C.;Royston, P.
在拟合回归模型时，数据分析师经常面临许多可能影响结果的预测变量。用于选择变量以识别“重要预测变量”子集的多种策略已经存在多年。模型构建的另一个问题是如何处理结果与连续预测变量之间关系的非线性。传统上，对于此类预测变量，假设分组后呈线性函数关系或阶跃函数。然而，线性假设可能不正确，导致最终模型指定错误。对于多变量模型，最近提出了一种基于分数多项式和向后消除相结合的系统方法来研究可能的非线性函数关系。到目前为止，该程序仅在 Stata 中可用，这肯定阻止了该有用程序的更普遍应用。将介绍该方法，将在两个示例中展示其优点，将说明当前 FP 功能的新方法，并且将很快介绍 SAS 中的宏。注意到 Stata 和 R 程序的差异。 (C) 2005 Elsevier B.V. 保留所有权利。
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 nonlinearity 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. (C) 2005 Elsevier B.V. All rights reserved.