Exemplos de uso de Multiple logistic regression em Inglês e suas traduções para o Português
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The multiple logistic regression model was used.
The statistical model used was the multiple logistic regression.
Multiple logistic regression adjusted by sex and age groups.
Table 3 shows the multiple logistic regression models.
Multiple logistic regression was used for multivariate analysis.
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Table 3 refers to the final multiple logistic regression model.
Multiple logistic regression for smoking as a dependent variable.
Then, we carried out the hierarchical multiple logistic regression analysis.
Multi-level multiple logistic regression with the following variables.
Confounder control was performed by multiple logistic regression.
A hierarchical multiple logistic regression analysis was performed.
Exploratory analysis included bivariate analysis and multiple logistic regression.
Non-conditional multiple logistic regression analysis was utilized.
Dentulous and edentulous individuals were compared andanalyzed separately using multiple logistic regression.
Table 3 shows the result of the multiple logistic regression model adjustment.
A multiple logistic regression model was proposed for the selected variables p.
The independent variables were analyzed using multiple logistic regression only when presenting a p-value of.
Multiple logistic regression analysis was performed, adjusted for confounders.
Bivariate analysis(chi-square test, p 0.05) and multiple logistic regression analysis were performed.
Multiple logistic regression multivariate analysis was conducted with MACE as a dependent variable.
The adjustment of possible confounding variables was performed using multiple logistic regression.
We conducted a multiple logistic regression analysis, with a significance level of 10.
Statistical analysis was performed using Chi-square test and multiple logistic regression.
Multiple logistic regression was used to test independent correlates for the presence of CAD.
Predictors of fatal outcome were subjected to multiple logistic regression, and the results are expressed as odds ratios.
Multiple logistic regression was used with the variables listed resulting in the risk score Table 2.
Therefore, a multiple logistic regression model with fewer independent variables was proposed.
Multiple logistic regression analysis identified independent risk factors and confounders.
The results for multiple logistic regression were expressed by odds ratio OR and 95% confidence interval 95%CI.
Multiple logistic regression analysis was used to identify independent predictors of late presentation.