Joint models for mixed categorical outcomes: a study of HIV risk perception and disease status in Mozambique

DOI10.1080/02664763.2017.1391184OpenAlexW2765614586WikidataQ56014058 ScholiaQ56014058MaRDI QIDQ5036448

Edwin Wouters, Osvaldo Loquiha, Marc Aerts, Nafissa Osman, Niel Hens, Herman Meulemans, Marleen Temmerman, Emilia Martins-Fonteyn

Publication date: 23 February 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://hdl.handle.net/10067/1463520151162165141

zbMATH Keywords

mixed modelsbivariate categorical datacontinuation-ratio logitsHIV infection statusperceived risk of HIV

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Uses Software

• BDM

Cites Work

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• Longitudinal data analysis using generalized linear models
• Models for discrete longitudinal data.
• Marginal regression models for the analysis of positive association of ordinal response variables
• Multivariate logistic models
• Parameterization of Multivariate Random Effects Models for Categorical Data
• Joint modelling of mixed outcome types using latent variables
• Regression Models for a Bivariate Discrete and Continuous Outcome with Clustering
• Multilevel models with multivariate mixed response types

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