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Related Experiment Video

Updated: May 26, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Examining the BMI-mortality relationship using fractional polynomials.

Edwin S Wong¹, Bruce C M Wang, Louis P Garrison

¹Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, WA, USA. eswong@uw.edu

BMC Medical Research Methodology

|December 30, 2011

Summary

This summary is machine-generated.

A flexible modeling approach revealed nonlinear relationships between body mass index (BMI) and mortality. This method, using multivariable fractional polynomials (MFP), identified distinct J-shaped and U-shaped BMI-mortality patterns for women and men, respectively.

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Last Updated: May 26, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Epidemiology
Biostatistics

Background:

Previous studies on body mass index (BMI) and mortality often use restrictive functional forms, leading to inconsistent findings.
A flexible, data-driven approach is needed to accurately model the complex relationship between BMI and mortality.

Purpose of the Study:

To investigate a flexible, data-driven modeling approach to determine the nonlinear and asymmetric functional form for BMI in relation to mortality.
To compare this novel approach against commonly used regression models for BMI-mortality estimation.

Main Methods:

Utilized data from the National Health Interview Survey (1997-2000) linked to the National Death Index (mortality follow-up through 2005).
Employed the multivariable fractional polynomials (MFP) procedure to identify the optimal functional form for BMI in logistic regression models for 5-year all-cause mortality.
Compared MFP model fit against linear-quadratic BMI models and categorized BMI models using a deviance difference test, stratifying analyses by sex.

Main Results:

The best-fitting model for BMI included powers -1 and -2.
The multivariable fractional polynomials (MFP) approach revealed a J-shaped BMI-mortality curve for women and a U-shaped curve for men.
The MFP model demonstrated a statistically significant improvement in fit compared to other models, with notable differences in curve shape, nadir, and mortality estimates.

Conclusions:

The multivariable fractional polynomials (MFP) approach offers a robust alternative to standard BMI categorization or linear-quadratic models.
This flexible method accurately captures the nonlinear and asymmetric BMI-mortality relationship, providing more precise estimates.
The MFP approach is valuable for studying the impact of the full spectrum of BMI values on various health outcomes and costs.