Our model contained age, lumen size, Fisher grade, and WFNS grade as predictors.
However, the best fitting model contained: age, gender, age*gender, East Midlands, Scotland, population density and non-car ownership.
Our data support the concept that adiponectin is an important marker in the pathogenesis of atherosclerosis since adiponectin remains significantly associated with plaque morphology in a fully adjusted multivariate model containing age, sex, BMI, hypertension, diabetes mellitus, smoking, family history of CAD, LDL-C, HDL-C, triglycerides, hsCRP, medication and PAT volume.
We first fit a model containing age and sex (model 1).
A model containing age, sex and deprivation alone explained 28% of the uncertainty.
A model containing age, sex, age-by-sex interaction, deprivation and GP practice alone explained 22% of the observed uncertainty.
Population density was associated with a statistically significant improvement in model fit (P = 0.0001) compared with the model containing age, gender and age*gender, but was not significant when compared with the model containing age, gender, age*gender and Townsend score (P = 0.1388).
Table 6 gives the RRs for the best fitting model containing age, gender, age*gender, population density and non-car ownership.
The strongest association with the BASFI (square root transformed) was found in a model containing age and CRP levels as independent variables (Table 3, Model 2).
We were able to explain 39% of the variance in the DI with a model containing age, sex, waist circumference, and three metabolic factors (Table 2).
Upon comparing the multivariate logistic regression model containing age and BE status with the nested model containing age alone, the multivariate model did not provide significantly better fit by the likelihood ratio test, although the p value was minimally greater than 0.05 (chi-square test = 3.55, corresponding to a p value of 0.0595).
