Bayesian Latent‐Class Mixed‐Effect Hybrid Models for Dyadic Longitudinal Data with Non‐Ignorable Dropouts

DOI10.1111/biom.12100zbMath1419.62301OpenAlexW1912565260WikidataQ30575084 ScholiaQ30575084MaRDI QIDQ5408012

Wen-Yi Wang, Ying Yuan, Jaeil Ahn, Suyu Liu

Publication date: 8 April 2014

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3970927

zbMATH Keywords

longitudinallatent classdyadicnon-ignorable missingnessmixed-effect

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Estimation in survival analysis and censored data (62N02)

Cites Work

• Unnamed Item
• Unnamed Item
• Random-Effects Models for Longitudinal Data
• Bayesian modeling longitudinal dyadic data with nonignorable dropout, with application to a breast cancer study
• Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model
• Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model
• Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
• A General Class of Pattern Mixture Models for Nonignorable Dropout with Many Possible Dropout Times
• Missing Data in Longitudinal Studies
• Mixed‐Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• A class of pattern-mixture models for normal incomplete data
• Analysis of Multivariate Longitudinal Outcomes With Nonignorable Dropouts and Missing Covariates
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Estimating a Treatment Effect from Multidimensional Longitudinal Data
• Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes
• Reparameterizing the Pattern Mixture Model for Sensitivity Analyses Under Informative Dropout
• Bayesian Measures of Model Complexity and Fit
• Pattern-Mixture Models for Multivariate Incomplete Data
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Informative Drop-Out in Longitudinal Data Analysis
• An Approximate Generalized Linear Model with Random Effects for Informative Missing Data
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies
• Marginal analyses of longitudinal data with an informative pattern of observations
• Parameterization and Bayesian Modeling
• Linear mixed models for longitudinal data

This page was built for publication: Bayesian Latent‐Class Mixed‐Effect Hybrid Models for Dyadic Longitudinal Data with Non‐Ignorable Dropouts