A general GEE framework for the analysis of longitudinal ordinal missing data and related issues

DOI10.1177/1471082X17752753OpenAlexW2790272882MaRDI QIDQ5142235

Fabio N. Demarqui, José Luiz Padilha da Silva, Enrico Antônio Colosimo

Publication date: 30 December 2020

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

Full work available at URL: https://doi.org/10.1177/1471082x17752753

zbMATH Keywords

proportional odds modelcorrelation coefficientmissing at randomlocal odds ratiodoubly robust GEE

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• R
• SPSS
• SAS
• geepack
• repolr
• MICE
• PROC GENMOD
• multgee
• SimCorMultRes

Cites Work

• Longitudinal data analysis using generalized linear models
• GEE for longitudinal ordinal data: comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN
• A product-multinomial framework for categorical data analysis with missing responses
• Semiparametric theory and missing data.
• GEE for Multinomial Responses Using a Local Odds Ratios Parameterization
• Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates
• Generalized Estimating Equations for Ordinal Data: A Note on Working Correlation Structures
• Marginal Regression Models for Clustered Ordinal Measurements
• Multiple Imputation and its Application
• Generalized Estimating Equations for Ordinal Categorical Data: Arbitrary Patterns of Missing Responses and Missingness in a Key Covariate
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data
• Multiple imputation of discrete and continuous data by fully conditional specification
• A Generalized Estimating Equation Method for Fitting Autocorrelated Ordinal Score Data with an Application in Horticultural Research
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