Examples of using Multivariable analysis in English and their translations into Portuguese
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Medicine
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
In the multivariable analysis, binary logistic regression was used.
However, birth weight lost its statistical significance in the multivariable analysis.
Multivariable analysis was performed through Poisson regression.
Logistic regression was used for multivariable analysis, and the design effect was taken into consideration.
Variables with p<0.20 in the bivariate analysis were selected for the multivariable analysis.
A multivariable analysis was also developed using a multiple linear regression.
With the aim of obtaining independent predictors of perioperative AMI, multivariable analysis was performed.
In the multivariable analysis, we used poisson regression with robustness of variance.
It is recommended that these associations be confirmed by a multivariable analysis, controlling confounding factors.
For the multivariable analysis(Table 4), it was decided to construct three different models.
Chi-square test was used to compare proportions andpoisson regression with robust variance in the multivariable analysis.
In the final multivariable analysis, the BP outcome was explained by two factors: age and smoking.
The variables with p< 0.20 in the bivariate analysis were considered possible confounders and were included in the multivariable analysis.
The multivariable analysis consisted of logistic regression models with calculations of adjusted ORs.
Exploratory analysis of risk factors, including analysis of correlation,preceded the multivariable analysis of the outcomes.
In multivariable analysis, these variables remained associated to the consumption of psychiatric drugs.
No association was observed between health related behaviors and central obesity after multivariable analysis in men and women from Florianópolis.
In the multivariable analysis Table 3, only APACHE II and lung disease were implicated in a higher death risk.
Variables that showed an association with the outcome characterized by a p value<0.25 on univariable analyses were selected for the multivariable analysis.
The multivariable analysis was employed in order to identify complication predictor factors in the intraoperative period.
All collected data was analysed by means of a statistical tool allowing for multivariable analysis to be performed per job category and also per office location.
Then, a multivariable analysis was performed for variables considered in the basic causes Model 1, adjusted for age and sex.
In multivariable analysis, age, interval between studies, and previous PCI were independent predictors of CAD progression.
Multivariable analysis indicated that stunted growth was associated with birth weight and the number of prenatal consultations.
Multivariable analysis was performed using Poisson regression, adjusted for the sampling design effect and stratified by sex.
In the multivariable analysis, advanced age and living with a partner remained associated with the greatest prevalence of the outcome.
In the multivariable analysis, female gender and the presence of common mental disorders remained associated with the use of benzodiazepines.
For the hierarchical multivariable analysis, Poisson regression with robust variance was used, as the prevalence of the outcome was high.
In the multivariable analysis, only female sex and presence of CMDs remained independently associated with seeking health services Table 2.
In the multivariable analysis the results presented are adjusted for gender, obesity, maternal education level, and cat and dog exposure.