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These variables were used to make the final prediction model using multivariate logistic regression, which will be used to develop their Minimizing ICU Readmission (MIR) score.
aAdjusted for all included predictors in model using multivariate logistic regression analysis.
Dropout will be analysed by (non) parametric longitudinal analysis techniques (eg GLM model) using multivariate statistics.
The final step in the analysis was the composition of a predictive model using multivariate logistic regression adjusting for only the confounding factors that were obtained in step two of the analysis.
Even though IL-β, STNFR1, STNFR2 and adiponectin individually predict delirium moderately well (area under the receiver operating characteristics curve: 0.70 to 0.84), the authors did not mention the overall area under the receiver operating characteristics curve of the model using multivariate regression analysis.
A logistic regression model using multivariate analysis showed T-bil (hazard ratio 83.3; 95 % CI 0.001 0.69; P for trend = 0.032) and HVPG (hazard ratio 0.011; 95 % CI 0.33 0.87; P for trend = 0.011) as independent significant risk factors for aggravation of EV after B-RTO (Table 3).
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Both functions are modeled using multivariate probability distributions, where in particular so-called copulas are helpful.
The methodology is based on the identification of data-based models using multivariate statistical methods such as Partial Least Squares (PLS).
When a quantitative phenotype such as growth rate under stress is considered, regression modeling using multivariate techniques such as partial least squares (PLS) is often used to identify metabolites correlated with the target phenotype.
It is possible to develop predictive models using multivariate regression analyses.
Subgroup-specific incidence rates were computed and modelled using multivariate logistic regression.
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