Mixed‐Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout

DOI10.1111/j.1541-0420.2008.01102.xzbMath1165.62088OpenAlexW2158901239WikidataQ33365207 ScholiaQ33365207MaRDI QIDQ3636993

Ying Yuan, Roderick J. A. Little

Publication date: 30 June 2009

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2027.42/66099

zbMATH Keywords

longitudinal datamissing datashared-parameter modelnonignorable dropout

Mathematics Subject Classification ID

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

Related Items (9)

Application of sensitivity analysis to incomplete longitudinal CD4 count data ⋮ Bayesian modeling longitudinal dyadic data with nonignorable dropout, with application to a breast cancer study ⋮ Identification problem of transition models for repeated measurement data with nonignorable missing values ⋮ Bayesian Latent‐Class Mixed‐Effect Hybrid Models for Dyadic Longitudinal Data with Non‐Ignorable Dropouts ⋮ Compliance Mixture Modelling with a Zero‐Effect Complier Class and Missing Data ⋮ Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout ⋮ A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits ⋮ Generalized shared-parameter models and missingness at random ⋮ Comments on: Missing data methods in longitudinal studies: a review

Cites Work

• Unnamed Item
• Random-Effects Models for Longitudinal Data
• Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model
• Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Nonignorable Dropout
• Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
• Model for the Analysis of Binary Longitudinal Pain Data Subject to Informative Dropout through Remedication
• A class of pattern-mixture models for normal incomplete data
• Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Mixed Effects Logistic Regression Models for Longitudinal Binary Response Data with Informative Drop-Out
• Selection Models and Pattern‐Mixture Models for Incomplete Data with Covariates
• 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
• Modelling Progression of CD4-Lymphocyte Count and Its Relationship to Survival Time
• An Approximate Generalized Linear Model with Random Effects for Informative Missing Data
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies

This page was built for publication: Mixed‐Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout