Pattern-mixture models for categorical outcomes with non-monotone missingness

DOI10.1080/00949650903062566zbMath1432.62363OpenAlexW2135829666MaRDI QIDQ3070609

Geert Molenberghs, Ivy Jansen

Publication date: 3 February 2011

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/1942/11408

zbMATH Keywords

categorical dataidentifying restrictionsmultivariate Dale modelnon-monotone missingnesspattern-mixture models

Mathematics Subject Classification ID

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

Related Items (4)

Rejoinder to the comments on: Missing data methods in longitudinal studies: a review ⋮ A Brief Review of Approaches to Non‐ignorable Non‐response ⋮ Nonignorable Models for Intermittently Missing Categorical Longitudinal Responses ⋮ Marginalizing pattern-mixture models for categorical data subject to monotone missingness

Uses Software

• Fahrmeir

Cites Work

• Multivariate statistical modelling based on generalized linear models. With contributions by Wolfgang Hennevogl
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• Monotone missing data and pattern‐mixture models
• Marginal Modeling of Correlated Ordinal Data Using a Multivariate Plackett Distribution
• A class of pattern-mixture models for normal incomplete data
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• Selection Models and Pattern‐Mixture Models for Incomplete Data with Covariates
• Pattern-Mixture Models for Multivariate Incomplete Data
• Informative Drop-Out in Longitudinal Data Analysis
• An Application of Maximum Likelihood and Generalized Estimating Equations to the Analysis of Ordinal Data from a Longitudinal Study with Cases Missing at Random
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies
• A pattern-mixture odds ratio model for incomplete categorical data
• Strategies to fit pattern-mixture models
• Linear mixed models for longitudinal data

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