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Binary logistic regression modelling incorporating transrectal biopsy parameters showed no statistically significant predictive variable.

The use of a binary logistic regression model revealed two SNPs that are predictive of AIEC phenotype (Table 5).

Binary logistic regression analysis confirmed intervention effects exceeded confounding effects.

Binary logistic regression was employed as a model to predict AIEC pathotype according to the nucleotide present in a particular SNP position.

Binary Logistic regression analysis showed that the age, H-Y stage, depression, anxiety and autonomic dysfunction increased the risk of constipation in PD patients.

Interestingly, two of the identified SNPs were adequate for the prediction of the AIEC phenotype as determined by the binary logistic regression model.

Binary logistic regression was applied [18].

Table 4 Binary logistic regression results.

Both descriptive analysis and binary logistic regression were conducted.

Binary logistic regression was selected to model the hypotheses.

Table 1 gives the results of the binary logistic regression.