CoChat
Sign Up
Cited by 1,066Statistical Methods in EpidemiologyStatistical Methods and ApplicationsAdvanced Causal Inference Techniques

The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.

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

Royston, P., Ambler, G., Sauerbrei, W.·1999·International Journal of Epidemiology
DOI 10.1093/ije/28.5.964
Open this paper in CoChat — free account

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/ije/28.5.964

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 44 references

Research works printed but not linked: 2

  1. Draper D. Assessment and propagation of model uncertainty (with discussion). J R Stat Soc, Ser B 1995;57:45-97.
  2. Elswick RK Jr, Schwartz PF, Welsh JA. Interpretation of the odds ratio from logistic regression after a transformation of the covariate vector. Stat Med 1997;16:1695-703.

Cited by

Sorted by citations

Items 701–800 of 1,000

Related

See all

Metadata

Authors
Royston, P., Ambler, G., & Sauerbrei, W.
Affiliations
  • Imperial College London (GB)
  • Hammersmith Hospital (GB)
  • University of Freiburg (DE)
ORCID
Venue
International Journal of Epidemiology
Publisher
Oxford University Press (OUP)
Published
1999-10-01
Volume
28
Issue
5
Pages
964-974
ISSN
  • 0300-5771 (ISSN-L)
  • 1464-3685
DOI
10.1093/ije/28.5.964
OpenAlex
W2105087430
Cited by
1,066
References
44
Citations by year
2026: 15 · 2025: 26 · 2024: 30 · 2023: 31 · 2022: 36 · 2021: 51 · 2020: 51 · 2019: 41 · 2018: 67 · 2017: 80 · 2016: 46 · 2015: 66 · 2014: 63 · 2013: 67 · 2012: 56
FWCI
8.54
Type
journal article
Language
en
Open access
closed
Locations
  • International Journal of Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
Funders
Wellcome Trust
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
fractional polynomials, risk transformation, risk curves, confounding variables, nonparametric regression, British civil servants
MeSH terms
Coronary Disease, Cross-Sectional Studies, Epidemiologic Methods, United Kingdom, Humans, Male, Models, Cardiovascular, Mortality, Neoplasms, Prospective Studies, Risk Factors, Sensitivity and Specificity, Linear Models, Odds Ratio, Risk Assessment, Stroke

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review