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Coefficients of predictor variables by ordinal regression.
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Preliminary models were fitted with random variables included also in the regression coefficients of predictors, but the improvements due to these additional parameters were marginal.
Random parameter models allow the coefficients of predictors to vary across the countries/regions per an analyst-specified continuous distribution (such as the normal distribution in this case) (Anastasopoulos and Mannering 2011; Venkataraman et al. 2013; Mannering et al. 2016).
The major reasons for leveraging the random parameter modeling technique in our research are summarized as follows: 1. Random parameter models allow the coefficients of predictors to vary across the countries/regions per an analyst-specified continuous distribution (such as the normal distribution in this case) (Anastasopoulos and Mannering 2011; Venkataraman et al. 2013; Mannering et al. 2016).
Confounders were retained if they changed the beta coefficients of predictors by greater than 10%.
Where numbers allow, we will explore coefficients of predictors of outcome.
Variables were retained in the final model if they added significantly to the likelihood of models or to the estimated coefficients of predictors.
Further inclusion into model 3 of either CRP or IL-6 did not add significantly to the models or to the estimated coefficients of predictors.
All variables were included in a logistic regression analysis and retained in the final model if they added significantly to the likelihood of models or to the estimated coefficients of predictors, as assessed using the –2 log likelihood ratio test.
The ratios of the coefficients of predictors to the smallest coefficient were therefore all between 1 and 2. We assigned a score of 1 to unilateral CAP, CIMT ≥1.0 mm, age ≥67 years and BMI <22 kg/m, and a score of 2 to significant CAD, bilateral CAP, CKD stage ≥3 and four or more AHM.
*Estimated coefficient of predictor on dependent variable and result from Wald test of statistical significance.
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