A note on marginalization of regression parameters from mixed models of binary outcomes

DOI10.1111/biom.12707zbMath1415.62098OpenAlexW2606555047WikidataQ38828470 ScholiaQ38828470MaRDI QIDQ108258

Robert D. Gibbons, Stephen H. C. Du Toit, Hakan Demirtas, Donald Hedeker, Stephen H. C. du Toit, Robert D. Gibbons, Hakan Demirtas, Donald R. Hedeker

Publication date: 20 April 2017

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc5650580

zbMATH Keywords

longitudinal dataregression parametersclustered datamixed modelsmultilevel modelspopulation-averaged estimatessubject-specific estimates

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

Marginal additive models for population‐averaged inference in longitudinal and cluster‐correlated data, A population‐averaged approach to diagnostic test meta‐analysis, brmsmargins, Marginalized random-effects models for clustered binomial data through innovative link functions, Unit-Lindley mixed-effect model for proportion data

Uses Software

• SuperMix

Cites Work

• Models for discrete longitudinal data.
• The Generalized Estimating Equation Approach When Data Are Not Missing Completely at Random
• Mixed Models
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Applied Mixed Models in Medicine
• Longitudinal Data Analysis
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