Mfpa: Extension of mfp Using the ACD Covariate Transformation for Enhanced Parametric Multivariable Modeling. (March 2016)

• Record Type: Journal Article
• Authors: Royston, Patrick; Sauerbrei, Willi
• Is Part Of: Stata journal. Volume 16:Number 1(2016)
• Page Start: 72
• Page End: 87
• Publication Date: 2016-03
• Subjects: st0425 -- mfpa -- mfp -- continuous covariates -- sigmoid function -- ACD transformation -- multivariable fractional polynomials -- regression models; Statistics -- Periodicals; Statistics -- Computer programs -- Periodicals; 001.422
• DOI: 10.1177/1536867X1601600109
• Languages: English
• ISSNs: 1536-867X

Abstract

In a recent article, Royston (2015, Stata Journal 15: 275–291) introduced the approximate cumulative distribution (ACD) transformation of a continuous covariate x as a route toward modeling a sigmoid relationship between x and an outcome variable. In this article, we extend the approach to multivariable modeling by modifying the standard Stata programmfp. The result is a new program, mfpa, that has all the features ofmfp plus the ability to fit a new model for user-selected covariates that we call FP1( p 1, p 2 ). The FP1( p 1, p 2 ) model comprises the best-fitting combination of a dimension-one fractional polynomial (FP1) function of x and an FP1 function of ACD ( x ). We describe a new model-selection algorithm called function-selection procedure with ACD transformation, which uses significance testing to attempt to simplify an FP1( p 1, p 2 ) model to a submodel, an FP1 or linear model in x or in ACD ( x ). The function-selection procedure with ACD transformation is related in concept to the FSP (FP function-selection procedure), which is an integral part ofmfp and which is used to simplify a dimension-two (FP2) function. We describe themfpa command and give univariable and multivariable examples with real data to demonstrate its use.