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 ...

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 ...

Marginal models for multivariate binary data permit separate modelling of the relationship of the response with explanatory variables, and the association between pairs of responses. When the former ...

"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 ...

Increased triglyceride-glucose (TyG) index values are strongly associated with decreased lung function in healthy individuals.

Researchers used 2018 data from the National Health Interview Survey to investigate the association. Men taking anxiety and depression medications were more likely to undergo prostate specific antigen ...

A 29 question-based cross-sectional survey was developed to explore knowledge and practices of predatory publishing and analyzed using descriptive statistics and binary logistic regression. Four ...