Distinction Between Two Statistical Terms: Multivariable and Multivariate Logistic Regression

Bayesian multivariate logistic regression.

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...

Tutorial on Multivariate Logistic Regression

Logistic Regression for Multivariate Longitudinal Ordinal Data

Variable Selection for Multivariate Logistic Regression Models

In this paper, we use multivariate logistic regression models to incorporate correlation among binary response data. Our objective is to develop a variable subset selection procedure to identify important covariates in predicting correlated binary responses using a Bayesian approach. In order to incorporate available prior information, we propose a class of informative prior distributions on th...

Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism.

OBJECTIVE Genetic programming is a search method that can be used to solve complex associations between large numbers of variables. It has been used, for example, for myoelectrical signal recognition, but its value for medical prediction as in diagnostic and prognostic settings, has not been documented. STUDY DESIGN AND SETTING We compared genetic programming and the commonly used logistic re...