Journal of Medical Statistics and Informatics

Journal of Medical Statistics and Informatics

ISSN 2053-7662
Original Research

Examining the association between C-Reactive protein and obesity by using the fractional polynomial approach; applying on NHANES dataset from 2001 to 2010

Ghada Abo-Zaid1,2* and Karyn Morrissey1

*Correspondence: Ghada Abo-Zaid (or)

1. European Centre for Environment and Human Health, University of Exeter Medical School, Knowledge Spa, Royal Cornwall Hospital, Truro, Cornwall TR1 3HD United Kingdom.

Author Affiliations

2. Faculty of Commerce- Ain Shams University, Khalifa El-Maamon St, Abbaisiya Sq., Cairo 11566, Egypt.


Objective: This study uses a flexible nonlinear approach, Fractional polynomial models (FPs), to examine the association between obesity and C-reactive protein to select the best fitted model within 44 potentially FP models.

Methods: Data for 5 years (2001-2010) of the National Health Interview Survey (NHANES) was used. All respondents aged between 17 and 74 were included in the analysis. CRP was transformed to ln(CRP) to eliminate skewness and missing values were removed from the analysis. A fractional polynomial approach was applied to measure the relationship between elevated levels of CRP and obesity. A closed test was used to select the best model among the 44 models.

Results: The best fitted fractional polynomial regression model contained the powers -2 and -2 for BMI. The association between the ln(CRP) and BMI when estimated using the FP approach exhibited a J-shaped pattern for women and men. Women have a higher risk of elevated CRP level compared to men. A deviance difference test yielded a significant improvement in model fit of -2 and -2 compared to other BMI functions.

Conclusion: The fractional polynomial regression model is the most robust estimator of BMI compared to other linear or nonlinear models.

Keywords: Categorization, C-reactive protein, fractional polynomial model, linear model, obesity

ISSN 2053-7662
Volume 5
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