title: Fractional polynomials and model selection in generalized estimating equations analysis, with an application to a longitudinal epidemiologic study in Australia.
abstract: 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.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia. jisheng.cui@deakin.edu.au
authors.forename: Jisheng
authors.initials: J
authors.lastname: Cui
authors.forename: Nick
authors.initials: N
authors.lastname: de Klerk
authors.forename: Michael
authors.initials: M
authors.lastname: Abramson
authors.forename: Anthony
authors.initials: A
authors.lastname: Del Monaco
authors.forename: Geza
authors.initials: G
authors.lastname: Benke
authors.forename: Martine
authors.initials: M
authors.lastname: Dennekamp
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Musk
authors.forename: Malcolm
authors.initials: M
authors.lastname: Sim
mesh_terms: D000328:Adult; D000367:Age Factors; D000535:Aluminum; D001315:Australia / Q000453:epidemiology; D002327:Cariostatic Agents / Q000009:adverse effects*; D005459:Fluorides / Q000009:adverse effects*; D005541:Forced Expiratory Volume; D006801:Humans; D008137:Longitudinal Studies; D008171:Lung Diseases / Q000139:chemically induced / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology; D008297:Male; D008669:Metallurgy; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D009784:Occupational Diseases / Q000139:chemically induced / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology; D016273:Occupational Exposure / Q000009:adverse effects*; D012129:Respiratory Function Tests / Q000379:methods; D012307:Risk Factors; D014797:Vital Capacity
chemical_list: D002327:Cariostatic Agents; D000535:Aluminum; D005459:Fluorides