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 ...

"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count data. The most ...

This article introduces a new type of logistic regression model involving functional predictors of binary responses, and provides an extension of this approach to generalized linear models. The ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

A class of conditional logistic regression models for clustered binary data is considered. This includes the polychotomous logistic model of Rosner (1984) as a special case. Properties such as the ...

Results from this study demonstrated use of a risk assessment model that used seven predictors of pulmonary embolism allowed investigators to identify patients at highest risk for this complication.