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We aimed to build a multivariable model including the best general ICU score, the best biological variable of hepatic failure, and the most relevant therapy performed at ICU admission.
A forward stepwise selection process was used to build a multivariable model for OS.
All the variables studies and VPI score were entered to build a multivariable model using covariance analysis (ANCOVA).
After univariable analysis we will build a multivariable model to predict endometrial carcinoma using the patient characteristics.
To build a multivariable model, we first calculated Spearman's correlation coefficient among scales and covariates to assess collinearity of potential correlates.
The age (n = 6) and occupation (n = 9) distribution cluster groupings were used to build a multivariable model of dog ownership by backward elimination.
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According to these results, we built a multivariable model including the SOFA score, INR, and RRT.
We built a multivariable model using log binomial regression for possible explanation, adjusting for biologic and known confounding factors for racial association with disease and health conditions.
Second, we built a multivariable model with standard logistic regression including only significant variables from the bivariable analysis.
We aimed to build a multivariable logistic regression model with a maximum of 12 predictors.
We built a multivariable regression model by sequentially adding and testing statistically significant candidates from the univariable analysis, in order of effect size.
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