Examples of using Multivariate regression analysis in English and their translations into Portuguese
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the 95% confidence interval adjusted by the multivariate regression analysis were used in the meta-analysis
the study employed the Pearson's correlation coefficient and the multivariate regression analysis.
The multivariate regression analysis was performed by six(50%)
Moreover, in multivariate regression analysis, we found an association between serum leptin with BMI
The meta-analysis included only those studies that assessed the outcome all-cause mortality comparing the fourth quartile of cystatin C with the first quartile and that conducted multivariate regression analysis of Cox proportional hazards.
Multivariate regression analysis showed that, both among men and women, physical inactivity while
The multivariate regression analysis showed no influence of the type of therapeutic approach on the probability of death or the occurrence of BPD36wks alone,
In multivariate regression analysis, after correcting for the interaction between diabetes mellitus and chronic renal disease, the only variable that remained associated with
Stepwise multivariate regression analysis of serum hsCRP levels
In the multivariate regression analysis after adjustment for age,
Using anterograde multivariate regression analysis, these three variables accounted for 59% of the variation in the distance covered on the 6MWT, IC alone accounting for 38%, and the use of oxygen and IC together accounting for 55.
univariate regression model p< 0.05 were included in the backward stepwise multivariate regression analysis to determine independent predictors of ECG ischemia grade.
their significance remained in the multivariate regression analysis.
used in the multivariate regression analysis(Erceg-Hurn& Mirosevich, 2008) are based on.
The identification that pacifier use in the previous 24 hours was the greatest risk factor in the multivariate regression analysis is a result that requires interpretation in a wider context,
In turn, the multivariate regression analysis, which suppressed the confounding effects,
The multivariate regression analysis model, having the MAC-Q scores as dependent variable
linear multivariate regression analyses were performed using diabetes,
hierarchical causal models, multivariate regression analyses and calculations analogous to the ones used to assess population attributable risks were applied.
For the analysis of the determinants of secular trends, hierarchical causal models, multivariate regression analyses and calculations analogous to the ones used to assess population attributable risks were applied.