Exemplos de uso de Multivariate logistic regression models em Inglês e suas traduções para o Português
{-}
-
Colloquial
-
Official
-
Medicine
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
The association between the independent variables andhw were tested using hierarchical multivariate logistic regression models.
The multivariate logistic regression models were built to predict factors of difficulty and the success in each of the tasks.
The effectiveness of the combination of the three markers to distinguish between malignant and benign effusion was evaluated by constructing multivariate logistic regression models.
Multivariate logistic regression models were applied to the sample to identify independent mortality predictors of the scores.
To verify the association of tmc with the variables of demographic andoccupational characteristics were constructed multivariate logistic regression models and hierarchical.
Multivariate logistic regression models were used to verify the predictors related to the occurrence of cardiotoxicity over time.
The effect of potential confounding variables was examined by including each variable sequentially into the multivariate logistic regression models and observing if the estimated ORs varied by more than 10.
Two multivariate logistic regression models were built to identify the predictors of two endpoints, cardiac arrest and hospital mortality.
In relation to the predictors of UI, only variables that presented statistically significant differences in the Chi-square test were included in the multivariate logistic regression models stepwise.
The analysis was rendered by means of multivariate logistic regression models that considered the existing hierarchy among the characteristics studied.
Nine multivariate logistic regression models were performed including all the EGRI variables and adding one at time the variables of the SB questionnaire.
The effects of having a diagnosis of hypertension ordiabetes on the adoption of healthy practices were analyzed by multivariate logistic regression models, using sex, age, and educational level as control variables, and the following outcomes: current use of tobacco products; regular physical activity during leisure time; recommended intake of fruits and vegetables; perception of low salt intake; frequent consumption of sweets; and excessive alcohol consumption.
Multivariate logistic regression models using stepwise selection were applied to determine the independent association of baseline characteristics and AE incidence.
Multivariate logistic regression models adjusted by children's age, with weighing and design effect were used to estimate association between mother's characteristics and introduction of artificial milk.
Then, different multivariate logistic regression models were constructed to assess whether associations existed between each event and the selected characteristics, when controlled by other variables.
In the third stage, multivariate logistic regression models were performed, with weighting and design effect and adjusted for infant age, with the variables and interactions significant at a 20% level in the bivariate analysis;
Multivariate logistic regression models were used to assess associations of liver markers with the risk of prevalent MS and with its improvement after weight loss, taking into account potential confounders.
Multivariate logistic regression models were used to assess factors associated with asthma at age 6 and AVB at 3, 6, 9, and 12 months, and in the 1st year of life, considering the main confounding variables and those with p-values.
Unpaired multivariate logistic regression models estimated the strength of association in terms of Odds Ratio OR, with respective confidence intervals of 95% 95%, between the dependent variable self-reported morbidity and the domains of quality of life in tertiles, using the upper tertile of each domain as a reference.
In the third stage, multivariate logistic regression models were performed, with weighting and design effect and adjusted for infant age, with the variables and interactions significant at a 20% level in the bivariate analysis; variables and interactions significant at a 5% level were maintained in the final multivariate model. .
Multivariate logistic regression models were developed using Odds Ratio OR and respective Confidence Intervals of 95% 95% CI as a measure of association, considering as outcome variable the diet quality self-perception as“good” and testing as predictive factors the independent variables with p-value< 0.20 in the univariate analysis.
In adjusted multivariate logistic regression models for boys, the probability of diet quality self-perceived as“good” was higher for adolescents who were physically active OR 2.38, those reporting consumption of vegetables>= 5 times/week OR 1,94, those with knowledge about healthy food OR 4.87, those with a satisfactory meal profile OR 5.15 and irregular meal profile OR 2.92 and those who had scores above the 75 percentile for total BHEI-R OR 1.59 Table 4.
These variables were included in a multivariate logistic regression model Table 3.
Table 4 Final multivariate logistic regression model associated with the presence of aspiration.
This association remained even when analyzed in a multivariate logistic regression model.
A multivariate logistic regression model was generated, including all variables presenting a p value< 0.1 in the univariate analysis.
From this univariate analysis,we used multivariate logistic regression model with duration of CPB and ischemia to evaluate whether the two variables are important determinants of mortality.
The multivariate logistic regression model was adjusted with the stepwise selection process to obtain the best model. .
Variables associated with the outcome with p¿0.20 in univariate analyzes were included in a multivariate logistic regression model for each age, with criteria for remaining in the final model p¿.
The multivariate logistic regression model was used to obtain the prognostic factors for prolonged MV 18.