Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model

DOI10.1002/BIMJ.201900051zbMath1436.62572OpenAlexW3000300768WikidataQ92530815 ScholiaQ92530815MaRDI QIDQ3299128

Katherine J. Lee, Matteo Quartagno, M.D. Hamidul Huque, Margarita Moreno-Betancur, Julie A. Simpson, John B. Carlin

Publication date: 17 July 2020

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

Full work available at URL: https://discovery.ucl.ac.uk/id/eprint/10093966/1/Quartagno_multiple%20imputation%20for%20random%20effect%20model.pdf

zbMATH Keywords

clustered datamissing datamultiple imputationjoint modelingrepeated measurementfully conditional specification

Mathematics Subject Classification ID

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

Related Items (1)

Evaluation of approaches for accommodating interactions and non‐linear terms in multiple imputation of incomplete three‐level data

Uses Software

• MICE

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