Fractional polynomials and model selection in generalized estimating equations analysis, with an application to a longitudinal epidemiologic study in Australia

Cui, Jisheng, de Klerk, Nick, Abramson, Michael, Del Monaco, Anthony, Benke, Geza, Dennekamp, Martine, Musk, Arthur W. and Sim, Malcolm 2009, Fractional polynomials and model selection in generalized estimating equations analysis, with an application to a longitudinal epidemiologic study in Australia, American journal of epidemiology, vol. 169, no. 1, pp. 113-121.


Title Fractional polynomials and model selection in generalized estimating equations analysis, with an application to a longitudinal epidemiologic study in Australia
Author(s) Cui, Jisheng
de Klerk, Nick
Abramson, Michael
Del Monaco, Anthony
Benke, Geza
Dennekamp, Martine
Musk, Arthur W.
Sim, Malcolm
Journal name American journal of epidemiology
Volume number 169
Issue number 1
Start page 113
End page 121
Publisher Oxford University Press
Place of publication United States
Publication date 2009
ISSN 0002-9262
1476-6256
Keyword(s) Epidemiology
Polynomials
Mathematics
Models
Estimating techniques
Summary In epidemiologic studies, researchers often need to establish a nonlinear exposure-response relation between a continuous risk factor and a health outcome. Furthermore, periodic interviews are often conducted to take repeated measurements from an individual. The authors proposed to use fractional polynomial models to jointly analyze the effects of 2 continuous risk factors on a health outcome. This method was applied to an analysis of the effects of age and cumulative fluoride exposure on forced vital capacity in a longitudinal study of lung function carried out among aluminum workers in Australia (1995-2003). Generalized estimating equations and the quasi-likelihood under the independence model criterion were used. The authors found that the second-degree fractional polynomial models for age and fluoride fitted the data best. The best model for age was robust across different models for fluoride, and the best model for fluoride was also robust. No evidence was found to suggest that the effects of smoking and cumulative fluoride exposure on change in forced vital capacity over time were significant. The trend 1 model, which included the unexposed persons in the analysis of trend in forced vital capacity over tertiles of fluoride exposure, did not fit the data well, and caution should be exercised when this method is used.
Language eng
Field of Research 111706 Epidemiology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ┬ęThe Author 2008. Published by the Johns Hopkins Bloomberg School of Public Health.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017342

Document type: Journal Article
Collection: Public Health Research, Evaluation, and Policy Cluster
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