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Quantitative characteristics were entered as continuous variables in univariate and multivariable analysis on DFS and DSS.
In bi- and multivariable analysis two-way interactions between variables were tested for significance and removed if P > 0.05.
Table 3 presents variables associated with moderate or poor perceived health in univariable and multivariable analysis.
All the variables in the score showed a strong statistical association with SNPT in the bivariate and multivariable analysis, except for miliary pattern in the CXR.
Poisson regression with robust estimation of variance was used in bivariate and multivariable analysis to estimate the prevalence ratios (PR) of the independent variables (socioeconomic and demographic factors) in relation to the outcomes, with the dependent variables (dietary patterns) being classified as dichotomous: 1st, 2nd and 3rd quartiles of consumption and high consumption (4th quartile).
We found no significant association between education and breast cancer after adjusting for other variables in multivariable analysis.
Co linearity analysis was done among the physical, psychological and environmental quality of life domain predictor variables as well as variables in the multivariable analysis.
As explained earlier the variables for the multivariable analysis were selected based on theory and prior research.
The odds ratios (OR) for the independant variables in the multivariable analysis are listed in tables 3 and 4.
Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for each variable in the multivariable analysis.
We adjusted for potential confounding variables in our multivariable analysis.
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CEO of Professional Science Editing for Scientists @ prosciediting.com