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An adaptive Bayesian logistic regression model (BLRM) with overdose control (EWOC) was used to guide dose escalation.

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

NAFLD was determined by a previously described algorithm and a multivariable logistic regression model determined predictors of CVD.

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.

A multivariable logistic regression model was constructed to determine if anticoagulation was an independent predictor of wound complication.

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

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

Wang L, Park HJ, Dasari S, Wang S, Kocher J-P, Li W. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model.

We first developed an unconditional polytomous logistic regression model treating the degree of vaccine hesitancy (low, medium or high) as the outcome of interest.

A multivariable logistic regression model assessed factors associated with delirium.

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