Exemplos de uso de Binary logistic regression analysis em Inglês e suas traduções para o Português
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The variables presenting p<= 0.25 were included in the binary logistic regression analysis Enter method.
Finally, we performed a binary logistic regression analysis including the main risk factors for extubation failure.
All statistically significant variables were evaluated for inclusion in a binary logistic regression analysis.
Binary logistic regression analysis was performed with the enter method in order to identify predictors of lower 6MWD.
Different characteristics between the groups including age, gender, dyslipidemia,smoking were subjected to Binary Logistic Regression Analysis.
The simple binary logistic regression analysis revealed an association between low scores on the Glasgow scale and the occurrence of PU p.
We explored the association of rs13010956 with the MetS phenotype using binary logistic regression analysis adjusting for sex, age, and smoking.
The simple binary logistic regression analysis revealed an association between total length of stay and the occurrence of PU p 0.015.
To verify association between practiceof walking outcome and environmental characteristics independent variables, binary logistic regression analysis was used.
Simple binary logistic regression analysis was used to test for the presence of any association between total length of stay and PU occurrence.
For the"vocal symptoms" outcome analysis between independent variables the chi-square test was used.a multiple binary logistic regression analysis p.
By binary logistic regression analysis, it was demonstrated that both variables gender and maternal smoking were independently associated with clefts.
The assumption of linearity in the logit scale log-odds between each quantitative covariable andthe binary response variable in binary logistic regression analysis was assessed by examination of smoothed scatter plots.
Binary logistic regression analysis was used to determine the strength of the associations between the dependent and independent variabl.
Factors that were significantly different between groups were input into a binary logistic regression analysis, using the Wald stepwise backward technique and odds ratios OR and 95% confidence intervals 95%CI were calculated.
Binary logistic regression analysis and the χ2 test were used for categorical variables to assess the odds ratio[OR] for depression and anxiety presence associated with gender.
Therefore, in order to estimate the relative risk of adolescents classified in decreasing groups of EEDaily who present compromising plasma lipid lipoproteins concentrations, binary logistic regression analysis was applied.
Univariate binary logistic regression analysis was applied and variables with a significance probability of -.20 or less were used as co-variables in the multivariate model.
The linearity assumption of the logit scale log-odds between each quantitative covariate andthe binary response variable in the binary logistic regression analysis were evaluated using fractional polynomials and building the smoothed scatter plots.
Binary logistic regression analysis was performed, while adjusting for sex, age, and smoking, and presented as odds ratios ORs; 95% confidence intervals CIs.
Otologic symptoms andbruxism maintained statistical significance in the binary logistic regression analysis, which demonstrated a 1.7 fold and twofold greater chance of such individuals have temporomandibular disorder, respectively.
Binary logistic regression analysis Table 3 identified two variables that were independently associated with lower 6MWD: age? -0.09; OR 0.92; p 0.002 and BMI? -0.15; OR 0.86; p 0.034.
Other 106 variables related to complications during the gestational period, labor and neonatal period were assessed as to their outcome, and six of them had a p value< 0.05 hypernatremia, hypercalcemia, hypoalbuminemia, atelectasis, respiratory failure and presence of infection, however,none of them was statistically significant after binary logistic regression analysis Table 5.
It is worth emphasizing that the results from the adjusted binary logistic regression analysis indicated that female adolescents presented approximately a 2.3-fold greater chance of having lumbar pain than their peers.
The extent to which behavioral risk factors insufficient levels of physical activity, excessive intake of fat and cholesterol and smoking were associated with biological risk factors overweight, imperiling levels of blood pressure and of serum lipids andlipoproteins was estimated using binary logistic regression analysis.
Univariate binary logistic regression analysis was performed to calculate odds ratios, while multivariate binary logistic regression was used to calculate adjusted odds ratios.
Serum s-Fas levels were the only predictors independently associated with red blood cell transfusion in critically ill patients in the first 28 days of follow-up when transferrin saturation and serum levels of ferritin, IL-6 andIL-10 were included in the model binary logistic regression analysis; Table 3, and the only variable associated with the number of red blood cell units transfused multiple linear regression; Table 4.
Odds Ratio OR values,established through binary logistic regression analysis, also controlling the BMI values, were used in order to establish estimates concerning the related risk with adolescents who present atherogenic risk lipid lipoprotein profile due to the categorization in decreasing groups of daily physical activity practice.
The crude odds ratio OR values from the binary logistic regression analyses confirmed the results from the analyses on the chi-square test.
Binary logistic regression analyses were used to evaluate the association of HW with cardiometabolic risk factors, in each model, as possible confounders.