Exemplos de uso de Multiple logistic regression was used em Inglês e suas traduções para o Português
{-}
-
Colloquial
-
Official
-
Medicine
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
Multiple logistic regression was used p< 0.05.
To study the impact of the family dysfunction variable on depressive symptoms the model of multiple logistic regression was used.
Multiple logistic regression was used for multivariate analysis.
In order to verify which variables best explained survival, multiple logistic regression was used, and Forward method was used to select those more closely associated with survival of patients.
Multiple logistic regression was used to test independent correlates for the presence of CAD.
Multivariate analysis via multiple logistic regression was used and the events were analyzed according to the occurrence of QoR-40.
Multiple logistic regression was used with the variables listed resulting in the risk score Table 2.
For the multiple analyses,in turn, multiple logistic regression was used, in the attempt to identify the association adjusted by confounding variables.
Multiple logistic regression was used to test associations between variables related to the grandmothers and the prevalence of breastfeeding.
For the final analysis of the main outcome, multiple logistic regression was used, with the occurrence of the fall or not as the outcome and the following predictive variables: gender and age.
Multiple logistic regression was used to identify the independent factors associated with death at home and calculation of adjusted odds ratios.
For the final analysis of the main outcome- the risk of falls- multiple logistic regression was used with the following predictive variables: gender, age group, occurrence of falls, with whom they live and some of the most prevalent comorbidities, considering 0.05 as level of significance.
Multiple logistic regression was used to express the level of association among variables, through odds ratio estimate Odds Ratio=OR and confidence interval of 95.
Subsequently, the multiple logistic regression was used to test the variables that showed an association p< 0.20 at the bivariate analysis.
Multiple logistic regression was used, in which the predictor variables that showed association with the outcome with a significance of p.
Once these variables were listed, multiple logistic regression was used in a backward selection process and all variables with a level of significance p< 0.05 were maintained in the model.
Multiple logistic regression was used to analyze associations with self-assessed poor health among the elderly individuals and with the information provided by the secondary informants.
Once these variables are listed, multiple logistic regression was used in process of backward selection, because it was the best method of adjusting the variables, keeping the model all variables with significance level P< 0.05.
Multiple logistic regression was used to obtain adjusted prevalence odds ratios OR and 95% confidence intervals 95% CI between asthma symptoms of the schoolchildren and parental smoking.
Multiple logistic regression was used to adjust for confounders, and the Wald test and likelihood ratio test were used to examine the significance of the models.
The multiple logistic regression was used with the listed variables, originating the recalibrated risk score based on the magnitude of the? coefficients of the logistic equation Table 3 and Table 4.
Multiple logistic regression was used through the estimate of odds ratio and 95% confidence intervals 95% CI to express the degree of association between the independent variables and the presence of pain.
Multiple logistic regression was used to determine the variables more strongly associated with perinatal conditions, necessity of mechanical ventilation, occurrence of infection and death.
Multiple logistic regression was used to assess the multiple relationships between extubation success and failure, need for reintubation or not and the participants' characteristics.
Multiple logistic regression was used to assess factors that showed association with the outcomes with p-values< 0.25 in the univariate analyses and to investigate independent predictors by controlling for possible confounders.
Multiple logistic regression was used to adjust the variables, whose criteria for inclusion was the association with the dependent variable in the bivariate analysis with a p-value< 0.20.
Multiple logistic regression was used to assess the association between exposure factors(gender, age, level of education, training time, number of patients seen per day, prior information on ergonomics at graduation, physical activity and the presence of joint damage and muscle aches) that are the outcome, with significantly 56.6%(n 162) have some kind of proven injury and 63.5%(n 181) has skeletal muscle disorder.
Multiple logistic regression was used for variable adjustment, whose criterion for variable inclusion was the association with the dependent variable in the bivariate analysis with p-value< 0.20. the variables were included in the regression analysis using the'enter' method, according to the decreasing value of odds ratio. the hosmer-lemeshow testwas used as a measure of quality-of-fit for the logistic regression models, in which a p-value> 0.05 indicates that the model is adjusted.
The chi-square test or Fisher's exact test, Student's t test and the Mann-Whitney test,ANOVA and multiple logistic regression were used.
Multiple logistic regressions were used to identify the predictive factors of occurrence of HAI in the ICU according to cause of admission.