Exemplos de uso de Multivariate analysis was carried out em Inglês e suas traduções para o Português
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Multivariate analysis was carried out using Poisson regression with robust variance.
After survey of the pre-and intra-operative data that associated with the emergence of some complication, multivariate analysis was carried out to find factors independently associated to complications.
Multivariate analysis was carried out for variables showing significant correlations.
In order to study the independent effect of the various factors, a multivariate analysis was carried out, using unconditional logistic regression with subsequent calculation of ORs and their respective 95% CIs.
A multivariate analysis was carried out with the following variables: BNP, age, etiology, renal function, LVEF and presence or absence of anemia.
In order to test whether these associations were present in all of the forms of clinical presentation of heart disease, multivariate analysis was carried out using multinomial logistic regression with the aid of the software Minitab 14.
A hierarchical multivariate analysis was carried out by means of conditional logistic regression.
In this work, a relation between physical activity and dyslipidemia was only evidenced in bivariate analysis, andits effect disappeared when the multivariate analysis was carried out, in accordance with agreeing on some international studies.
The multivariate analysis was carried out to identify predictors of health-related quality of life improvement.
The multivariate analysis was carried out by the logistic regression method, using the program SPSS 13.0 Chicago-IL, USA.
The multivariate analysis was carried out following a hierarchical conceptual model, including variables with a level of significance of.
Multivariate analysis was carried out using Poisson regression following a conceptual model and taking into account the study design effect.
A multivariate analysis was carried out using logistic regression analysis in which the initial model was composed of the variables with p.
Multivariate analysis was carried out using poisson regression with robust adjustment of variance. prevalence ratio was the effect measure used.
A multivariate analysis was carried out by adjusting a model of logistic regression that included all variables that had a p value< 0.25.
The multivariate analysis was carried out for 30-day survival, as there were not enough predictors with statistical significance at hospital discharge.
This multivariate analysis was carried out following a hierarchical model, and variables presenting p< 0.20 in the likelihood ratio test remained in the model.
A multivariate analysis was carried out to assess whether the relationship between the markers of myocardial necrosis and NT-proBNP levels was independent of ejection fraction.
Multivariate analysis was carried out sequentially by means of an initial selection of variables using elastic net regularization EM, which were analyzed by the Cox model.
Multivariate analysis was carried out step-by-step through the progressive inclusion of independent variables that could influence mortality. p values lower than 0.05 were considered significant.
Multivariate analysis was carried out with a two-level adjustment for the variables involved: first, there was an adjustment for gender, age, skin color/ethnic group, and overweight; and second, there was an adjustment for gender, age, skin color/ethnic group, overweight, and other behavioral factors included in the study as independent variables.
Multivariate analysis was carried out using Poisson regression, considering p< 0.20 for the control of confounding factors, whose variables analyzed were: gender; age; level of education; socioeconomic classification; physical activity; cigarette smoking; time of awareness of hypertension; awareness of healthy habits regarding the use of salt; regular visits to the doctor; alcohol use; and presence of DM.
Multivariate analysis was carried out using Poisson regression with robust variance, as a four level hierarchical model, including maternal age, educational level and socioeconomic status on level 1, parity, number of children at home and maternal employment on level 2, birth weight on level 3, and walking, talking, removing clothes, having been previously trained unsuccessfully, following orders, signaling the need to go to the toilet and having been given medical guidance on level 4.
Multivariate analyses were carried out using Poisson Regression, based on a conceptual model.
Multivariate analyses were carried out as to establish the independent value of variables with follow-up outcomes, by means of the analysis of Cox proportional hazards model.
The multivariate analyses were carried out to spatialize climatic, physical and socioeconomic variables and so differenciate the Brazilian States and Regions.
The variance and multivariate analysis were carried out considering complete randomized blocks due to its equivalence with the estimation for the lattice efficiency.
The univariate and multivariate analyses were carried out by conditional logistic regression, according to the hierarchical model.
Statistical analysis was carried out using multivariate models and, using internal consistency and construct validity for the validation of durel.
To identify associated factors, univariate and multivariate regression analyses were carried out using SPSS 17.0 software.