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In the adjusted model (all measures of in-hours access, adjusted for sociodemographic variables and including a random effect for general practice), there remained evidence (p<0.001) for independent associations between increased out-of-hours GP use and each of the four in-hours access measures (table 1).
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For each adjusted model all measured covariates were included except where noted in the table.
As predicted by the model, all measured genes were significantly impaired.
Before statistical modeling, all measures of FENO were log-transformed because the data were highly skewed.
Both experimental results and selected models showed that all measured properties were mainly influenced by the volume fraction of the dispersed particles.
We modeled the associations of % arsenic species biomarkers with body mass index, % body fat, fat free mass, and waist circumference categories in unadjusted regression models and in models including all measures of body composition.
In the univariate regression model, all SOD measures were statistically significantly associated with distance to TRI facilities (Table 3).
Later vaccination start times result in a convergence of the proportional and non-proportional models on all measures.
Covariates with a p-value > 0.15 in full models for all measures within the three sets of outcomes (hormone levels, semen quality, sperm DNA damage) were removed from the final models.
Use of the spatial error and spatial lag regression improved the fit of the models in all measures; improving the significance of independent variables, increasing R-squared (a pseudo- R in spatial regression) and the log-likelihood, and decreasing the Akaike info criterion (AIC) and Schwarz criterion relative to the OLS regression.
Starting with the full model, including all measured independent variables, the backward selection method was conducted manually.
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CEO of Professional Science Editing for Scientists @ prosciediting.com