Examples of using Confounding variables in English and their translations into Portuguese
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Potential confounding variables.
The authors, however,did not control for confounding variables.
Possible confounding variables pre- and postoperative were controlled.
Variations above 10% were considered as confounding variables.
To control confounding variables, logis-tic regression was performed.
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Most of these studies performed adjustment for confounding variables.
After adjusting for confounding variables, the association with dry cough was not significant.
Logistic regression was used to adjust for confounding variables.
Among the confounding variables, age entered the models as continuous variable. .
However, this association disappeared in the analysis adjusted for confounding variables.
After adjusting for potential confounding variables, statistically significant associations p.
Table 2 Bivariate analysis between PPD and potentially confounding variables in women.
However, other potential confounding variables could not be controlled for, with the data available.
In addition, causal inference can frequently be influenced by confounding variables.
The construction of the model took confounding variables and collinearity into account.
After this presence was observed,the final model was adjusted for confounding variables.
For the selection of the confounding variables, we also considered the current scientific knowledge in the literature.
In this situation, scientists would call these alternative explanations‘confounding variables.
Potential confounding variables included predisposing and enabling factors of the use of health services.
Initially, the stratification was performed to analyze the effect modification and confounding variables.
When presented with large amounts of confounding variables, people still tend to claim knowledge of the unknowable.
The attributable fraction estimate should be carefully interpreted due to possible confounding variables.
After adjustment for confounding variables, 13.3% of MVHA versus 11.0% of MNVHA reported induced abortion in their lifetime p> 0.05.
Some of the limitations found in the present study include the following confounding variables.
After the confounding variables were neutralized, a positive association between FHS and CS incidence was observed.
Multiple logistic regression was used to exclude the influence of possible confounding variables.
The first stage concerned the definition of confounding variables based on bivariate analyses using the Chi-square test.
Potential confounding variables identified by univariate analysis with p up to 0.10 were included in the multivariate models, as well as gender variable. .
The Mann Whitney U test showed no statistically significant differences between groups in any of the confounding variables and MMSE in the pre-intervention moment Table 1.
The selection of potential confounding variables for this analysis was based on the theoretical model of Andersen and Newman5 5.