Sentence examples for the model gender from inspiring English sources

In addition, the following predictors were used in the model: gender, meconium ileus, asthma, and the TGFb1 codon 10 CC genotype (previously shown to be associated with severe lung disease).

Two variables remained in the model, gender and practice size.

After including ethnicity, social class and age band in the model, gender was no longer a significant factor.

The independent variables were entered stepwise into the model: gender, age, interaction between gender and age, and finally, BMI.

As a result, once age, gender, education level, and physical function were all considered in the model, gender became statistically non-significant.

The additive and multiplicative models revealed similar results with regard to covariates selected to remain in the model: gender, race/ethnicity, and age.

In the models gender, ethnicity, smoking habits were included as categorical data together with the three genotypes.

We performed multivariate analysis with all available predictors of hospital mortality included in the models (gender, age, APACHE II, mechanical ventilation, surgical admission and diagnosis type).

We then used logistic regression to analyse associations between presence, type (spouse or other) and health status of primary coresident with the proportion of decedents dying at home, including in the models gender, age group and socioeconomic indicators.

In addition, we performed multivariate analysis where we adjusted for all available predictors of hospital mortality included in the models (gender, age, APACHE II, mechanical ventilation, surgical admission and diagnosis type) determined by backward elimination of non-significant variables.

Using this model, gender and the family history of the cancer were found as significant predictors.