Sentence examples for backward regression modeling was from inspiring English sources

Stepwise backward regression modeling was employed to identify potential determinants for adherence and persistence from available data on the study cohort.

All socio-demographic variables were included simultaneously in the model and a backward regression model was performed.

Cox backward regression model was used in multivariate analysis to assess the independency of the prognostic variables.

An exploratory backward regression model was created from this to determine if fewer variables could still explain a large portion of the variability in the data.

Linear regression and backward regression models were used to determine whether the additional information for all five polymorphisms altered the effect of genotype on enzyme activity.

A backward "conditional" regression model was used in the multivariate analysis with removal set at a 0.1 level of significance.

A backward stepwise regression model was also developed in order to identify which diastolic function variables, as measured within the study, besides age and BMI, predicted DD grading in our patients' population, with conduit, as a potential predictor, forced into the model.

A subsequent backward stepwise Cox regression model was undertaken to identify the most significant determinants of surgical delay beyond 36 h.

Backward Stepwise logistic regression model was fitted to identify different determinants of infant feeding practice.

A backward conditional logistic regression model was developed to describe risk factors for ARC in multivariate analysis.

To analyze ICU mortality, a backward stepwise logistic regression model was employed to choose from among variables that were associated with mortality and felt to be clinically important.