Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies

DOI10.1111/j.0006-341X.2000.01157.xzbMath1060.62570WikidataQ30633669 ScholiaQ30633669MaRDI QIDQ4670474

Thomas R. Belin, Minzhi Liu, Jeremy M. G. Taylor

Publication date: 22 April 2005

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

missing datamultiple imputationGibbs samplingmultivariate longitudinal data

Mathematics Subject Classification ID

Multivariate analysis (62H99) Applications of statistics to biology and medical sciences; meta analysis (62P10) Survival analysis and censored data (62N99)

Related Items (7)

A Bayesian approach towards missing covariate data in multilevel latent regression models ⋮ Screening for prostate cancer using multivariate mixed-effects models ⋮ Multiple imputation: current perspectives ⋮ Random covariances and mixed-effects models for imputing multivariate multilevel continuous data ⋮ Just‐Identified Versus Overidentified Two‐Level Hierarchical Linear Models with Missing Data ⋮ Comments on: Missing data methods in longitudinal studies: a review ⋮ Using multiple imputation with GEE with non-monotone missing longitudinal binary outcomes

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• Inference for the Association between Coefficients in a Multivariate Growth Curve Model

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