Exemplos de uso de Multinomial regression em Inglês e suas traduções para o Português
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Multinomial regression was used to control for confounders and to estimate measures of effect.
Table 5 shows the factors associated to simultaneous risk behaviors according to multinomial regression.
On the other hand, multinomial regression did not indicate a link between athletic internalization and restrictive eating.
Still, violation of the assumption of proportionality in the first two model options- verified with the Brant's test- led to the use of multinomial regression.
Statistical analysis used multinomial regression with crude and adjusted analysis, with results expressed as odds ratios and confidence intervals of.
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To evaluate factors associated with the need for care and determinants of change,the analysis of multiple logistic and multiple multinomial regression were used.
Then, analysis of single and multiple multinomial regressions were carried out to test the association of the factors studied with the occurrence of prenatal care visits.
Finally, to observe the association between independent variables anddifferent types of headache, multinomial regression model was used.
Held multinomial regression analysis poissson with robust adjustment of variance and estimated the prevalence ratio of previous causes of hospitalization with each variable.
We estimated the association between exposure and outcome by using multinomial regression models, stratified by gender, and eutrophic and overweight subgroups.
A multinomial regression model was fitted to estimate the odds ratio OR of each covariate on“no companion” and“partial companion” as compared with“continuous companionship”.
As stated in the research design section,we analyze the multinomial regression results based on the reference category, i.e., the classification probability in the Mature stage.
Multinomial regression analysis showed that only the variable age was associated with tension-type headache, and younger subjects 14-16 years were less likely to have this type of headache.
These two variables were also significant in predicting log AHI and were independently associated with sleep apnea ofany level of severity, as shown in the multinomial regression model.
The SM can be considered an extension of the multinomial regression model. It compares each category of the response variable with a reference category, normally the first or last category.
In the multinomial regression Table 2, among female respondents 20 years and older, the chance of suffering violence increased with age, their risk was 2.74 times higher than men's 95% CI: 1.52;4.94.
Thumbnail Table 4 Adjusted hierarchical analysis by multinomial regression of the factors associated to excess weight according to the weight-for-length/height criterion among under five year-old children who live in the six most populous cities, Maranhão state, 2006/2007.
However, after the multinomial regression adjusted for gender of adolescents and youth, the only variable that remained unrelated to the use of male condom was the involvement in drunkenness in the previous 30 days.
In the multinomial regression model, the significant variables relating to the family/home structure mother's marital status, father living with the child, head of the family and number of people in the home were firstly included.
However, after the multinomial regression adjusted for sex, the variables that remained were age, religion, mother's education, engaging in binge drinking in the last 30 days, age of the first dose, and alcohol consumption in the lifetime.
Multinomial regression analysis has shown, for different types of headache, that females have 15.61 times more chances of having tension headache and 72% less chances of classifying headache as“other types”.
In the multinomial regression we use the c-1 logit functions to apply the regression, with c being the number of categories of the dependent variable, so that the overall function of the conditional probability of the model for the 5 categories, according to, is presented in the equation 1.
The multinomial logistic regression has the following specification.
Table 3 shows the results of multinomial logistic regression.
Multinomial logistic regression was used in the multivariate analysis.
Multinomial logistic regression final model is presented in Table 2.
Multinomial logistic regression is useful for modeling probabilities of multiple category outcomes simultaneously.
Analytical procedures included descriptive statistics andmultivariate modeling binary and multinomial logistic regression.
The method is based on the multinomial logistic regression applied to a longitudinal structure data.
The analysis used multinomial logistic regression with a hierarchical model.