A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data

DOI10.1080/03610918.2013.818690zbMath1328.62333OpenAlexW2004986432MaRDI QIDQ5259172

A. F. Donneau, Murielle Mauer, Geert Molenberghs, Adelin Albert

Publication date: 24 June 2015

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/1942/18577

zbMATH Keywords

multiple imputationmissing at randomordinal variableslongitudinal analysis

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Software, source code, etc. for problems pertaining to statistics (62-04) General biostatistics (92B15) Generalized linear models (logistic models) (62J12) Random number generation in numerical analysis (65C10)

Related Items (4)

Estimating longitudinal change in latent variable means: a comparison of non-negative matrix factorization and other item non-response methods ⋮ Testing the proportional odds assumption in multiply imputed ordinal longitudinal data ⋮ Joint GEEs for multivariate correlated data with incomplete binary outcomes ⋮ A general GEE framework for the analysis of longitudinal ordinal missing data and related issues

Cites Work

• Unnamed Item
• Longitudinal data analysis using generalized linear models
• A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data
• Some simple methods for generating correlated categorical variates
• Inference and missing data
• A Potential for Bias When Rounding in Multiple Imputation

This page was built for publication: A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data