She used antecedents like the personality traits, role model, age, gender, education, and experience to predict the social entrepreneurial intention.
*The following variables were entered in the model age, gender, APACHE II score on admission, admission diagnosis category, chronic comorbidities, mechanical ventilation, and admission physiological and clinical variables (coma, heart rate > 150 beats/minute, SBP < 90 mm HG, and AKI).
Multivariable logistic regression was performed (with the following covariates forced into the model: age, gender, baseline weight, drug regimen, WHO Stage and log CD4 count) using the PASW (SPPS version 17; SPSS Inc, Chicago, USA) statistical package.
In the univariate model, age, gender, being happy/not thinking about one's weight and comorbidity were associated with misperception.
aIndicates patient level risk adjustment model (age, gender, admission status, APR-DRG severity of illness, and AHRQ comorbidities).
In a logistic regression model age, gender, BMI and a history of hypertension were important determinants of LVH (p < 0.05).
Variables known to be important for osteoporosis were included in the final models: age, gender, smoking and menopausal status.
No significant effect of the other covariates included in the models (age, gender and fusion) was observed.
Potential confounding variables known to be associated with response variable were included in multivariable models (age, gender, BMI).
In multivariate models, age, gender and factors that showed significant association in univariate analysis (pN and grade of differentiation) were included.
Apart from serum selenium levels, the following variables were included in the models: age, gender, smoking, body mass index (BMI), TSH concentrations, thyroid nodules, and thyroid echogenicity.
