Examples of using Multiple model in English and their translations into Portuguese
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Colloquial
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Official
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Medicine
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Financial
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
To integrate the multiple model.
In the multiple model, all variables with p.
Among men, six variables remained in the multiple model.
For entry in the multiple model were considered variables with p¿0.20.
Variables with p<0.25 were included in the multiple model.
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Table 2 shows the final multiple model with correspondents HR unadjusted and adjusted.
All distal social determinants were included in multiple model 1.
In the multiple model, we included all the variables that presented p< 0.20 in the bivariate analysis.
The level of statistical significance considered for the multiple model was p.
The variables that were maintained in the final multiple model were maternal schooling, hemoglobin and CRP Table 4.
Variables with level of significance p<0.20 were selected to be included in the multiple model.
In the initial multiple model, the variables co-infection, diagnosis, sex, age and time of follow-up were included.
All variables with p<0.20 in the analysis of association with the HRQoL were included in the multiple model.
In the multiple model were included all variables in which the associative tests chi-square and Student's t had p-value.
Age and sex were used as adjustment variables during all stages of constructing the multiple model.
We did not identify any multiple model, as only one of the variables was identified in the multiple analysis.
Variables that achieved a significance level of 0.20 in the univariate analyses were included in the multiple model.
In the multiple model, high psychological demand, low social support and extensive working hours remained associated with MPD Table 5.
Therefore, these values are not statistically significant for any of the variables inserted in the final multiple model.
In addition to the variables with up to 0.20 significance in the bivariate, the final multiple model was adjusted for gestational age and for birth weight.
Bivariate analyses were performed and variables presenting a descriptive level below 0.20 p<0.20 were selected for the multiple model.
The variable externalizing symptoms p-value=0.683 did not show significance at the multiple model, remaining in the model as control variable.
Table 3 presents the final multiple model with the following significant variables: gender, self-reported health and education, adjusted for income, use of tobacco and age.
The univariate models indicate which variables should be explored in the multiple model, to verify the correlation.
The exclusion of"household wealth index" from the multiple model reduced the prevalence ratio of"having a literate father or stepfather" by more than 10.
In multiple model 2, in addition to the significant distal social variables of model 1 p< 0.05 in the Wald test, biological factors that reached p< 0.20 in the simple models were included.
Then, the most strongly associated variables(p< 0.20) were included in a second final multiple model, when p< 0.05 was used to consider the association between exposure and outcome.
In the adjusted multiple model, those who flew for more than 65 hours a month showed increased 78% chance of unintentionally sleeping while flying the airplane when compared to pilots who had average monthly flight time of 65 hours or less.
When the variables that represent the conditions at the beginning of life were excluded from the multiple model, the effects of the socioeconomic and lifestyle variables on mortality showed almost no change.
The multiple model estimates indicate that immediate risk of discontinuing treatment is 25.0% higher among women aged under 40, and 22.0% higher in women with stage IV cancer, compared with stages 0, I and II and no information Table 4.