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Model selection for univariable fractional polynomials.

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PubMed
Summary
This summary is machine-generated.

Fractional polynomials offer flexible regression modeling. This study introduces fp_select, a tool to help researchers select parsimonious fractional polynomial models using a closed test procedure for improved data analysis.

Keywords:
continuous covariatefp_selectfractional polynomialsmodel selectionregression modelsst0488

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Fractional polynomials (FP) are increasingly used for flexible parametric modeling of regression relationships.
  • The method was introduced by Royston and Altman in 1994 and has gained significant traction.
  • Selecting an appropriate FP model can be complex, requiring specialized tools.

Purpose of the Study:

  • To introduce fp_select, a novel postestimation tool for fractional polynomial analysis.
  • To provide a method for selecting parsimonious fractional polynomial models.
  • To demonstrate the application of fp and fp_select using real-world data.

Main Methods:

  • The study presents the fractional polynomial selection procedure (also known as function selection procedure).
  • This closed test procedure is implemented within the fp_select postestimation tool.
  • The methodology involves selecting the most parsimonious FP model that adequately fits the data.

Main Results:

  • The fp_select tool facilitates the selection of appropriate fractional polynomial models.
  • The introduced selection procedure offers a systematic approach to model selection.
  • Examples illustrate the practical utility of fp and fp_select in real data analysis.

Conclusions:

  • fp_select is a valuable tool for researchers utilizing fractional polynomial regression.
  • The fractional polynomial selection procedure aids in developing parsimonious and interpretable models.
  • The presented methods enhance the application of flexible parametric modeling in various scientific fields.