Sentence examples for binary logistic regression identified from inspiring English sources

Binary logistic regression identified connectivity between nesting grounds and forest in 1991 as the best predictor of whether or not sites were abandoned by maleo over the next 12 years; a loss of local continuity between nesting grounds and forest is associated with a sixfold increase in the probability of abandonment.

In diabetic patients, univariable (binary) logistic regression identified the variables that are associated with the response to CRT.

The binary logistic regression identified older age, lower MMSE, neurological or psychiatric disease and lower preoperative ScO2 as independent predictors of postoperative delirium.

Multinomial logistic regression models adjusting for sociodemographic and clinical factors provided estimates of the association between BMI levels and treatment use; binary logistic regression identified correlates of CAM use.

Results from the binary logistic regressions identified that age [ 9, 48] and gender [ 9, 10] were significant determinants of the existence of at least one of the CVD risk factors in the low-income community.

Crude and adjusted results of multivariate binary logistic regressions identified the significant determinants of the existence of at least one of the CVD risk factors in the low-income communities.

Binary logistic regression analysis identified residents in large cities (>100 000 people) were 15% less likely to be sedentary for overall activity classifications in comparison to small town dwellers (<1000 people) (OR=0.85; 95%Cl=0.74 0.99).

Multiple binary logistic regression models identified characteristics independently associated with the use of analgesics or nutraceuticals.

By using binary logistic regression, we identified thrombocytopenia, hyponatraemia and anaemia to be of decreased significance as predictors of need for advanced supportive care.

The binary logistic regression model identified SD T) as the most significant and often the only predictor of known cancer genes, whereas the results for the other predictors we tested were inconsistent.

Furthermore, binary logistic regression analysis identified ethnicity as the only useful predictor of whether a patient would use CAM (odds ratio [OR] = 1.962; P = 0.028; 95% confidence interval [CI], 1.094, 3.516) (Table  4).