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Multivariate analysis including all significant variables in bivariate analysis (p < 0.05), except surgery during ICU stay, surgery (overall) and adequate surgery, identified 4 independent prognostic factors.
Comprehensive data on drinking behavior were collected and the analyses include bivariate and multivariate regression techniques.
Statistical analyses included bivariate and multivariate methods.
These included bivariate (chi-square) and multivariate (logistic regression) analysis to determine correlates or predictors of adherence.
Multivariate Poisson modelling included the significant bivariate variables and accounted for hospital clustering by using hierarchical models.
Statistical approaches include univariate, multivariate and time-series models [77].
The data availability from a sufficiently large number of studies allows to apply this multivariate approach, which requires reported estimates for the bivariate relations among all proxy variables.
The data analysis approaches include spectral assessment, as well as descriptive, bivariate and multivariate methods.
With the novel multivariate approach, we here performed the first genome-wide bivariate association analyses for obesity and osteoporosis.
For individuals with diabetes, we used a multivariate approach to examine the relationship between HFI and variables that were statistically significant, based on the bivariate analyses.
The general theory of bivariate hotspot detection is explained, including the bivariate binomial model, the multivariate exceedance approach, and the bivariate Poisson distribution.
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