We used multivariate analyses to control for diverse covariates.
First, the relatively small number of included children precluded multivariate analyses to be performed.
We used multivariate analyses to identify the main compositional trends of variation in pasture communities.
For clarity purposes, we have structured our multivariate analyses to correspond to the respective research questions proposed.
Moreover, we performed univariate and multivariate analyses to identify the risk factors associated with poor prognosis in the studied population.
Many prospective observational cohort studies have used multivariate analyses to identify predictors of PCS [6],[6],[6]],[6]].
We conducted multivariate analyses to assess the influences of various factors on survival 30 days from ED presentation, 90 days from ED presentation, and overall (Table 2).
Binary logistic regression model was further used for multivariate analyses to identify independent factors associated with CM use from the same variable tested in the bivariate analysis.
This was coupled with multivariate analyses to control for confounding.
The weights were not applied in the multivariate analyses to ensure unbiased results.
We also performed univariate and multivariate analyses to identify prognostic factors in this disease.
