Exemplos de uso de Logistic regression analyses em Inglês e suas traduções para o Português
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The logistic regression analyses were conducted using the SPSS 20 program.
Chicago, USA. The level of significance adopted in uni- and multivariable logistic regression analyses was 0.05.
In the logistic regression analyses, the variables were not significant.
The associations between genotypes of TIM-4 gene and RA were assessed by computing the odds ratio OR and95% confidence intervals 95% CI from logistic regression analyses adjusted for sex and age.
In the simple logistic regression analyses, the older age at menarche was a protection factor against PCOS.
It can also be difficult to disentangle the importance of the various measures of body size from the cut-points that were used to form the dichotomous categories for the logistic regression analyses Table 3.
We ran univariate and logistic regression analyses by excluding the subjects whose selected variables were missing.
Aiming to determine the factors independently associated with the development of AKI using the different diagnostic criteria studied,we developed a series of logistic regression analyses, which are presented in Table 3.
Binary logistic regression analyses, in gross odds ratio OR values, confirm the results of the Chi-Square analyses. .
To estimate the associations between color/race and the outcomes self-rated health status and functional incapacity,ordinal logistic regression analyses proportional odds type model were used to obtain odds ratios OR and respective 95%CI.
Therefore, logistic regression analyses were performed to further study the relationship between different measures of adiposity and MC.
The effect of residents' experience, patients' age, body mass index BMI, and level of insertion site on complication during TEC, procedure failure, andTEC related postoperative complications were analyzed by using Univariate Logistic Regression analyses.
The crude odds ratio OR values from the binary logistic regression analyses confirmed the results from the analyses on the chi-square test.
Logistic regression analyses were used in order to determine whether smoking was correlated with demographic and health risk behavior characteristics.
As a measure of the effect, odds ratio was used, applying logistic regression analyses for each dependent variable, adjusted for gender, age, employment status and municipality.
Logistic regression analyses were used to evaluate the association between the breakfast intake category and cardiometabolic risk factors in each model as possible confounders.
In cohort studies, in which data collection took place at determined time points, logistic regression analyses are performed by dividing breastfeeding durations into intervals that are processed separately, as if each time point was an individual cross-sectional study.
Logistic regression analyses showed that BMI, waist circumference, body fat percentage, and waist-to-height ratio were positively and significantly associated with low MC in both sexes, with the exception of waist-to-height ratio after adjustments for females Table 3.
We conducted uni and multivariate logistic regression analyses to determine the factors associated with the development of AKI for each of the criteria studied.
Logistic regression analyses showed that all different measures of adiposity were negatively and significantly associated with MC in both sexes, with the exception of waist-to-height ratio for females, after adjusting for cardiorespiratory fitness and maternal education level.
We used univariable logistic regression analyses as a screening tool to perform a backward stepwise multivariable model to identify independent predictors of dialysis and death.
We conducted logistic regression analyses by using hazard as an independent variable to measure risk, and cardiac death/Tx as the dependent variable.
Binary logistic regression analyses were used to evaluate the association of HW with cardiometabolic risk factors, in each model, as possible confounders.
Multiple logistic regression analyses were performed for each dependent variable described above, including the independent variables that obtained a level of significance of up to 15.
The Odds Ratios(OR)obtained from the logistic regression analyses were converted into Prevalence Ratios(PR) and their respective confidence intervals were estimated using the Delta method29.
Uni- and multivariate logistic regression analyses were used to assess the inflammatory marker role in predicting increased LAVi> 28 mL/m. The statistical significance level was set at p< 0.05.
In addition, results from logistic regression analyses of data from patients in the phase 1 trial, suggest a relationship between systemic exposure(AUC) and occurrence of arterial thrombotic events.
Main effects logistic regression analyses were used to establish the predictive relationship between continuous or dichotomized BNP levels and mortality adjusted for the effects of clinical and laboratory variables.
Finally, we carried out multiple logistic regression analyses to study the relationship between breastfeeding in the first years of life and school-age obesity with confounder control.
The logistic regression analyses were performed for all potential predictor variables of interest, with the values shown as hazard ratios HR within the 95% confidence interval 95% CI and shown Kaplan-Meier curves.