Skip to content

The use of fractional polynomials to model continuous risk variables in epidemiology

by Patrick Royston, Gareth Ambler, Willi Sauerbrei
International Journal of Epidemiology ()
Get full text at journal


BACKGROUND: The traditional method of analysing continuous or ordinal risk factors by categorization or linear models may be improved. METHODS: We propose an approach based on transformation and fractional polynomials which yields simple regression models with interpretable curves. We suggest a way of presenting the results from such models which involves tabulating the risks estimated from the model at convenient values of the risk factor. We discuss how to incorporate several continuous risk and confounding variables within a single model. The approach is exemplified with data from the Whitehall I study of British Civil Servants. We discuss the approach in relation to categorization and non-parametric regression models. RESULTS: We show that non-linear risk models fit the data better than linear models. We discuss the difficulties introduced by categorization and the advantages of the new approach. CONCLUSIONS: Our approach based on fractional polynomials should be considered as an important alternative to the traditional approaches for the analysis of continuous variables in epidemiological studies.

Cite this document (BETA)

Readership Statistics

189 Readers on Mendeley
by Discipline
54% Medicine and Dentistry
12% Agricultural and Biological Sciences
10% Mathematics
by Academic Status
31% Researcher
24% Student > Ph. D. Student
12% Student > Master
by Country
4% United States
3% United Kingdom
2% Germany

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Sign up & Download

Already have an account? Sign in