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For completeness, we also built a forth logistic regression model for the whole biopsy population (Additional file 1).
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For diabetes, a survey-weighted logistic regression model was fit.
Geographically weighted temporally correlated 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.
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.
This analysis is done by fitting a logistic regression model.
Hosmer DW, Lemeshow S. A goodness of fit test for the multiple logistic regression model.
Previous PostPrevious Geographically weighted temporally correlated logistic regression model.
A multivariable logistic regression model assessed factors associated with delirium.
Sci Rep. Geographically weighted temporally correlated logistic regression model.
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