A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data

DOI10.1016/j.csda.2007.04.020zbMath1452.62795OpenAlexW1972396618MaRDI QIDQ1023479

Geert Molenberghs, Caroline Beunckens, Cristina Sotto

Publication date: 12 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/1942/9691

zbMATH Keywords

asymptotic biasmissing at randommultiple imputation GEEweighted GEE

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Sampling theory, sample surveys (62D05)

Related Items (8)

Multiple imputation methods for handling missing values in longitudinal studies with sampling weights: Comparison of methods implemented in Stata ⋮ Joint GEEs for multivariate correlated data with incomplete binary outcomes ⋮ Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout ⋮ Doubly robust pseudo-likelihood for incomplete hierarchical binary data ⋮ A Bayesian transition model for missing longitudinal binary outcomes and an application to a smoking cessation study ⋮ Using multiple imputation with GEE with non-monotone missing longitudinal binary outcomes ⋮ Model selection of generalized estimating equations with multiply imputed longitudinal data ⋮ A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data

Uses Software

• Fahrmeir

Cites Work

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