Exemplos de uso de Bivariate logistic em Inglês e suas traduções para o Português
{-}
-
Colloquial
-
Official
-
Medicine
-
Financial
-
Ecclesiastic
-
Ecclesiastic
-
Computer
-
Official/political
Statistical analysis included chi-square test,student's t and regression bivariate logistic model, considering p.
Bivariate logistic regression models were built to check the isolated association between the dependent variable and each independent variable.
For data analysis, a univariate analysis was performed through Pearson's chi-square test, and bivariate logistic regression.
These associations were estimated by utilizing bivariate logistic regressions to calculate odds ratios ORs and the corresponding 95% confidence intervals.
The associations between the response variable andexposure variables were initially estimated using bivariate logistic regression.
The bivariate logistic regression analysis Table 1 showed the existence of an association between the presence of TMD and the presence of craniomandibular pain p>0.05.
To verify the existence of an association between TMD and control groups and craniomandibular andcervical pain, a bivariate logistic regression was applied.
The association between dichotomous variables was analyzed by bivariate logistic regression, and the kappa statistic was used to measure the concordance between the methods.
The association of the participants and treatment-related variables with extubation failure andreintubation was investigated using a bivariate logistic regression model.
Initially, bivariate logistic regressions were carried out aiming to identify interactions between the independent variables and PIPVG.
To assess the possible intervening variables, multivariate analysis was performed by the bivariate logistic regression stepwise forward, in which all pre-and perioperative factors were adjusted as possible confounding factor, with P.
Initially, bivariate logistic regressions were carried out aiming to identify interactions between the independent variables and PIPVG. Those which obtained a value of p.
The prevalence ratio between the MRP andthe independent variables was calculated using bivariate logistic regression models, considering the high MRP values as the risk category to calculate the prevalence ratio The model included the following predicting factors: BMI/CRF and BMI of the parents.
In the bivariate logistic regression analysis, the variables significantly associated with the presence of mental disorder were: age, marital status, skin color, pregnancy trimester, chronic disease and hospitalization to treat any clinical complication during the current pregnancy.
Categorical data were shown as absolute and relative frequencies andpossible associations were calculated using bivariate logistic regression models to test the isolated association between the dependent variables and each independent variable, in addition to analyzing the variables that entered the model, exploring the possible confounding factors and identifying the need for statistical adjustment of the analyses.
We performed bivariate logistic regressions with a confidence level of 95% between each of the sociodemographic variables and the corresponding dependent variable, either alcohol abuse and frequent consumption or traffic accident event.
Descriptive analysis of the data was carried out with the SPSS 20.0 software SPSS Inc., Chicago,United States, through a bivariate logistic regression gross analysis and subsequently the multiple logistic regression model was applied, with one of the categorical exit variables being dichotomous dependent variable and multiple explanatory variables independent, based on the theoretical reference of association of TB with socio-economic variables.
Bivariate logistic regression models were built to test the isolated association between the dependent variables and each independent variable, in addition to analyzing those that entered into the model, exploring possible confounding factors and identifying the need for statistical adjustment of analyses.
Bivariate logistic regression models were constructed to test the isolated association between the dependent variable and each independent variable, as well as to analyze the variables in the model, to explore the possible confounding factors and identify the need for statistical adjustment of analysis.
For the bivariate logistic analysis, the following variables were considered: gender, surgery, and use of invasive devices CVC and MV before the first episode of LCBI considered as case, previous notification of early onset sepsis, use of antimicrobials for less than three days for treatment of early suspected sepsis, and use of antimicrobials agents for at least seven days for treatment of early onset sepsis.
There were performed descriptive as well as bivariate and logistic regression analysis.
Associations between socioeconomic variables andhealth icsap were analyzed using bivariate and logistic regression.
The bivariate ordinal logistic regression was used to assess the association between each pattern and each socioeconomic, behavioral and maternal variable assessed.
For assessing the potential simultaneous effect of the several factors studied on clinical outcome,Cox's logistic bivariate regression was employed.
Analysis of bivariate binary logistic regression between each of the independent variables and the outcome under analysis low level of physical activity are presented in Table 2.