Marginalizing pattern-mixture models for categorical data subject to monotone missingness
From MaRDI portal
Publication:745343
DOI10.1007/s00184-008-0219-yzbMath1433.62041OpenAlexW2052964523MaRDI QIDQ745343
Ivy Jansen, Geert Molenberghs, Caroline Beunckens, Cristina Sotto, Geert Verbeke
Publication date: 14 October 2015
Published in: Metrika (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/1942/9265
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Models for discrete longitudinal data.
- Pattern-mixture models for categorical outcomes with non-monotone missingness
- A Local Influence Approach Applied to Binary Data from a Psychiatric Study
- Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model
- Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
- Inference and missing data
- Formalizing Subjective Notions About the Effect of Nonrespondents in Sample Surveys
- 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
- Sensitivity Analysis for Incomplete Contingency Tables: The Slovenian Plebiscite Case
- Pattern-mixture models with proper time dependence
- Selection Models and Pattern‐Mixture Models for Incomplete Data with Covariates
- Pattern-Mixture Models for Multivariate Incomplete Data
- 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
This page was built for publication: Marginalizing pattern-mixture models for categorical data subject to monotone missingness