A joint model for longitudinal continuous and time-to-event outcomes with direct marginal interpretation

DOI10.1002/bimj.201200159zbMath1441.62335OpenAlexW2113702086WikidataQ43767024 ScholiaQ43767024MaRDI QIDQ2857488

Achmad Efendi, Paul Dendale, Geert Molenberghs, Edmund Njeru Njagi

Publication date: 4 November 2013

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.201200159

zbMATH Keywords

frailtyWeibull distributionmarginalizationjoint modellinear mixed modelcombined model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (4)

Integrated nested Laplace approximation for the analysis of count data via the combined model: A simulation study ⋮ A joint normal‐binary (probit) model ⋮ A marginalized multilevel model for bivariate longitudinal binary data ⋮ Joint model for left-censored longitudinal data, recurrent events and terminal event: Predictive abilities of tumor burden for cancer evolution with application to the FFCD 2000-05 trial

Uses Software

• JM

Cites Work

• Unnamed Item
• Random-Effects Models for Longitudinal Data
• A combined overdispersed and marginalized multilevel model
• On the Weibull-Gamma frailty model, its infinite moments, and its connection to generalized log-logistic, logistic, Cauchy, and extreme-value distributions
• A family of generalized linear models for repeated measures with normal and conjugate random effects
• The frailty model.
• Models for discrete longitudinal data.
• Marginally Specified Logistic‐Normal Models for Longitudinal Binary Data

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