A new study investigated how logistic regression model training affects performance, and which features are best to include when examining datasets from individuals suffering from COVID-19.

The model was constructed on the basis of complete-case analysis. Simple logistic regression was used to identify potential predictors for paclitaxel HSR. Variables with a P value of <.05 were then ...

Statistical Analysis We developed a modified logistic-regression model for lung-cancer prediction in the PLCO control group of smokers.

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are ...

Misclassification of binary outcome variables is a known source of potentially serious bias when estimating adjusted odds ratios. Although researchers have described frequentist and Bayesian methods ...

The latter was derived from the C statistic of a logistic regression model, with second-year emergency hospital care as the outcome and first-year scale cut-offs as the predictors.