Sentence examples for multivariable model using from inspiring English sources

Cost estimates for intent, firearm type, and payer source were obtained from the multivariable model using the BYLEVEL option in conjunction with the LSMEANS statement.

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.

We refit the multivariable model using a dichotomous indicator for the DCIS risk groups.

Explanatory variables with a p value < 0.25 were included in the multivariable model using a stepwise descending method (backward).

Re-estimating the main multivariable model using a subset of 'severe' complaints produced very similar results to the main model.

All the variables studies and VPI score were entered to build a multivariable model using covariance analysis (ANCOVA).

Table 1 details multivariable models using the principle of backward selection for biomarkers at baseline and when assessed longitudinally using PFS and OS as outcomes.

We developed multivariable models using a difference-in-difference parameterisation.

Exact logistic regression was used for multivariable modeling using a forward stepwise model building approach.

Variables with a p-value <0.10 from the univariable analysis were then tested in multivariable models using forward stepwise procedures.

Nonetheless, similar results were obtained in multivariable models using the OGTT measurements in the subgroup from the 2005 2006 NHANES.