Multiple imputation for multilevel data with continuous and binary variables

DOI10.1214/18-STS646zbMath1397.62265arXiv1702.00971MaRDI QIDQ1799343

Stef van Buuren, Vincent Audigier, Shahab Jolani, Ian R. White, Matthieu Resche-Rigon, Matteo Quartagno, Thomas P. A. Debray, James Carpenter

Publication date: 18 October 2018

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1702.00971

zbMATH Keywords

missing datamultiple imputationmixed datajoint modellingmultilevel datafully conditional specificationsystematically missing values

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Paired and multiple comparisons; multiple testing (62J15)

Related Items (6)

Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures ⋮ Multiple imputation of ordinal missing not at random data ⋮ Modeling racial/ethnic differences in COVID-19 incidence with covariates subject to nonrandom missingness ⋮ Methods for integrating trials and non-experimental data to examine treatment effect heterogeneity ⋮ Application of iterative hybrid MI approach to household survey data with complex dependence structures ⋮ Imputation of Mixed Data With Multilevel Singular Value Decomposition

Uses Software

• R
• S-PLUS
• lme4
• Stata
• MICE
• MEMSS

Cites Work

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