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Multivariate analysis using logistic regression.

Various prognostic factors were analyzed by multivariate analysis using logistic regression.

Variables associated with outcome were identified by multivariate analysis using logistic regression.

A univariate and multivariate analysis using the Fischer and Mann–Whitney tests was performed.

Multivariate analysis using variables significant at univariate analysis and pre-eclampsia confirmed this difference.

Multivariate analysis using a GEE methodology identified the same independent factors associated with the presence of depressive symptoms (Table  5).

Finally, a multivariate analysis using logistic regression was performed to find variables independently associated with sampling failure.

Subsequently we performed a multivariate analysis using binary regression (OR with 95% CI) and significant p < 0.05.

Multivariate analysis using logistic regression was performed to evaluate significant predictors (determined on univariate analysis) that were associated with mortality.

Variables with a p-value less than 0.1 are included in the multivariate analysis, using logistic regression.

We performed a simple regression analysis and afterwards a multivariate analysis using the more statistically correlated measurements.