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Advanced multivariate methods can provide a clearer pattern of the complex impact of predictors.
Nonresponse might also have affected the selection and estimated prognostic impact of predictors of HRQL.
A logistic regression analysis was conducted to explore the impact of predictors of engaging in daily napping behaviour.
This model estimates the impact of predictors on the average OHS over the five follow-up time points.
We furthermore used an analytical approach (Generalized Additive Modelling) that is specifically designed to analyse data when the impact of predictors on the outcome variables are nonlinear.
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Linear regression models were used to evaluate the impact of predictor variables on continuous outcome variables.
We used the lower bounds of these ranges to identify clinically meaningful effects as an indication of the potential prognostic impact of predictor variables.
Univariate and multivariate (logistic) regression analysis were performed to assess the relative impact of predictor variables on the outcome variables, and a p-value of < 0.05 was considered statistically significant.
The univariate linear regression analysis was performed on all CRS subgroups, in order to test the impact of predictor variables IL-8, CRP, triglycerides concentrations (TG), total cholesterol (TC), low-density lipoproteins (LDL), and high-density lipoproteins (HDL) on the value of the dependent variable of PAI-1.
Hierarchical quantile Poisson log-normal regression is used to assess the most important predictors, variation in predictor effects by quantile, and varying impacts of predictors by region.
In order that variations in the strength of impacts of predictors can be straightforwardly compared, the GP practice deprivation score (with a range from 3.2 to 66.5) is transformed to a [0,1] scale using a linear transformation, ( frac{mathrm{IMD}-min left(mathrm{IMD}right)}{max left(mathrm{IMD}right -min left(mathrm{IMD}right -min
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CEO of Professional Science Editing for Scientists @ prosciediting.com