Logistic regression was used to develop a risk prediction model using the FIT result and screening data: age, sex and previous screening history.

We accommodate the longitudinal nature of the multiple outcomes with a unique extension of the nested random effects logistic model with an autoregressive structure to include drop-out and baseline ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

Peiming Wang, Martin L. Puterman, Mixed Logistic Regression Models, Journal of Agricultural, Biological, and Environmental Statistics, Vol. 3, No. 2 (Jun., 1998), pp ...

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.

By comparison, classical statistical models such as logistic regression (LR) rely on selection of risk factors, often on the basis of a priori knowledge. Although ML techniques have achieved recent ...