Shoukri MM, Collison K and Al-Mohanna F
Background: The metabolic syndrome is intimately linked hypertension, impaired glucose tolerance, dyslipidemia, and abdominal obesity and is associated with an increased risk of total and cardiovascular mortality in adults. Genetics as well as environmental influences have been implicated in obesity and several cardiovascular risk factors. Because Family is one of the most important factors affecting metabolic risk factors, studying co-aggregation of the components of the syndrome among family members, and in particular spousal pairs, is of interest to genetic epidemiologists and community health researchers. Methods: Based on the clinical definition of the syndrome, we introduce three statistical models to estimate the prime parameter of interest which measure the degree of clustering of the disease among spousal pairs. Since the focus in this paper is on the methodological approach to estimate the between pairs clustering parameters, we shall use Monte-Carlo simulated data for demonstration purposes, with values of the input parameters for each component taken from a well-known Korean study. We develop two models, the Bivariate Truncated Poisson Model (BTPM), which models the counts, and the Bivariate Dirichlet Multinomial Model (BDMM), which models the frequency of counts, and discuss the relative merits of each model. The two models are qualitatively different but quantitatively interrelated. Since the clinical definition of the metabolic syndrome requires that at least three of its components, co-exist within a subject, we show that adhering to this definition requires certain specifications that should be satisfied in any of the adopted models. We estimated the clustering parameters under the specified models. A comparison between the models was based on the internal consistency of each model. What we mean by that is the degree of closeness of the estimated distribution to the observed data. The BDMM fitted the data much closer than the BTPM. Interpretation: In a sample of randomly selected spousal pairs; and according to the clinical definition, the number of components of the metabolic syndrome can in an individual be 0, 1, 2, 3, 4, or 5. Estimation of the clustering parameter of the counts is equivalent to the estimation of the intraclass correlation coefficient (ICCC) between pairs. Assessing the goodness of fit of the proposed models, it is more statistically sound to estimate the degree of clustering of the components of the syndrome in spousal pairs under the BDMM.
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