Protective estimation of longitudinal categorical data with nonrandom dropout

DOI10.1080/03610929708831902zbMath0964.62514OpenAlexW4250456723MaRDI QIDQ4337302

Bart Michiels, Geert Molenberghs

Publication date: 12 March 2000

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929708831902

zbMATH Keywords

multiple imputationcontingency tableselection modelsmissing valuespseudo-likelihood estimationpattern-mixture models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Contingency tables (62H17)

Related Items (7)

Bayesian analysis for incomplete multi-way contingency tables with nonignorable nonresponse ⋮ Comparing diagnostic tests with missing data ⋮ Missing data mechanisms and their implications on the analysis of categorical data ⋮ A pattern-mixture odds ratio model for incomplete categorical data ⋮ Pattern-mixture models for categorical outcomes with non-monotone missingness ⋮ A Local Influence Approach Applied to Binary Data from a Psychiatric Study ⋮ A model for overdispersed hierarchical ordinal data

Cites Work

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• Pseudo-likelihood inference for clustered binary data
• Informative Drop-Out in Longitudinal Data Analysis
• Logistic Regression for Correlated Binary Data
• An Application of Maximum Likelihood and Generalized Estimating Equations to the Analysis of Ordinal Data from a Longitudinal Study with Cases Missing at Random
• Protecting Against Nonrandomly Missing Data in Longitudinal Studies

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