Examples of using Multiple logistic regression models in English and their translations into Portuguese
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Table 3 shows the multiple logistic regression models.
Multiple logistic regression models were performed with the presence of DR and DN as dependent variables.
Descriptive statistics were calculated, and two multiple logistic regression models were used, both of which were controlled for age.
The multiple logistic regression models were used in order to determine the effect of the variables analyzed in head and neck cancer.
The relationships between variables were analyzed in multiple logistic regression models, considering a 5% significance level.
Multiple logistic regression models were used to estimate adjusted odds ratios and their respective 95% confidence intervals.
That variable was classified as“very good or good” or“fair orinsufficient” certification, and two multiple logistic regression models were applied.
Two multiple logistic regression models with the same variable-response overweight+ obesity and same independent variables were performed.
Factors associated with mortality andsevere complications in the perioperative period were determined using multiple logistic regression models.
The chi-square test and multiple logistic regression models were used for the analysis of risk factors for death and length of hospital stay> 10 days.
In order to verify the occurrence of multicollinearity between ST-segment depression andelevation of cTnI two multiple logistic regression models were performed.
Based on the initial analysis, the multiple logistic regression models multivariate analysis were adjusted considering the age quantitatively and by age group.
Variables significant in the bivariate analyses were the first entered into the multiple logistic regression models, but all other variables were tested.
The multiple logistic regression models estimated included all significant variables identified in the crude analysis, respecting the hierarchical blocks previously described.
With the aid of IBM SPSS 20, univariate analysis was performed by means of Pearson's Chi-square test, andmultivariate analysis was done through unconditional Multiple Logistic Regression Models.
The results of the adjustment tests of the multiple logistic regression models Hosmer and Lemeshow showed a good fit for the final model Prob> chi 0.5722.
The data were analyzed using descriptive and inferential statistics, divided between two moments: gross univariate analysis usingPearson's non-parametric chi-square test, in which all independent variables were tested, and adjusted multiple analysis using non-conditioned Multiple Logistic Regression Models.
Multiple logistic regression models were estimated for consumption of alcohol and tobacco, selecting the variables that showed significance levels.
The verification of the epidemiological aspects was done using multiple logistic regression models or poisson regression models, which were selected according to the frequency of occurrence of ectoparasites.
Multiple logistic regression models were also adjusted for three possible outcomes: infection by any type of parasite; infection by helminths only; and infection by protozoans only.
After identifying significantvariables by bivariate analysis, these were entered into multiple logistic regression models to examine each variable, while controlling for all other confounding factors.
In order to obtain the four multiple logistic regression models which identify the risk factors for the IRDIs, we initially performed the Chi-square nonparametric test, in which the variables where p.
To better understand the predictorsof risk of death, two analyses of multiple logistic regression models were performed: one to identify demographic factors and associated in-hospital interventions, and another to identify the influence of major complications on death.
Associations between the variables of interest andabdominal obesity were verified using multiple logistic regression models, with WC>p80 being the outcome, and socioeconomic variables, maternal nutritional status and the child's individual variables being used to adjust the model. .
Age-adjusted ORs were calculated for males andfemales using multiple logistic regression models; each model included drinking or one of the forms of violence as the outcome variable of interest and one of the behavioral or psychological predictors and age as predictor variables.
The multiple logistic regression model was used.
Therefore, a multiple logistic regression model with fewer independent variables was proposed.
A multiple logistic regression model was proposed for the selected variables p.
Table 3 refers to the final multiple logistic regression model.
The Hosmer-Lemeshow test was used under the hypothesis that the multiple logistic regression model presents a well fit value p 0.8705.