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Geographically weighted temporally correlated logistic regression model.

For diabetes, a survey-weighted logistic regression model was fit.

This analysis is done by fitting a logistic regression model.

We took two approaches for each side effect model: a multivariate logistic regression model that included all variables, and a machine learning logistic regression model with feature selection.

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A multivariable logistic regression model assessed factors associated with delirium.

Sci Rep. Geographically weighted temporally correlated logistic regression model.

ORs and 95% CIs from an unconditional polytomous logistic regression model, adjusted for education and age.

Table 5 Binary logistic regression model for the SNPs associated with the AIEC pathotype.

Hosmer DW, Lemeshow S. A goodness of fit test for the multiple logistic regression model.

Wang, L. et al. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model.