Analysis of clustered ordinal data with subclusters via a bayesian hierarchical model

DOI10.1080/03610929808832170zbMath0901.62130OpenAlexW2047974162MaRDI QIDQ3842903

Ming Tan, Yinsheng Qu

Publication date: 2 December 1998

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

Full work available at URL: https://doi.org/10.1080/03610929808832170

zbMATH Keywords

clinical trialGibbs samplingmixed-effects modelrejection samplingvariance componentlongitudinal ordinal data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items (3)

The analysis of ordered categorical data: An overview and a survey of recent developments. (With discussion) ⋮ Is Each NPMLE of a Continuous Bivariate Distribution Function with Singly Right-Censored Data Really Inconsistent? ⋮ Mixed Effects Logistic Regression Models for Multiple Longitudinal Binary Functional Limitation Responses with Informative Drop-Out and Confounding by Baseline Outcomes

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Inference from iterative simulation using multiple sequences
• Analyzing Bivariate Ordinal Data Using a Global Odds Ratio
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• Models for Longitudinal Data: A Generalized Estimating Equation Approach
• Practical Markov Chain Monte Carlo
• A Mixed-Model Procedure for Analyzing Ordered Categorical Data
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Marginal Modeling of Correlated Ordinal Data Using a Multivariate Plackett Distribution
• Marginal Regression Models for Clustered Ordinal Measurements
• The Analysis of Longitudinal Polytomous Data: Generalized Estimating Equations and Connections with Weighted Least Squares
• A Random-Effects Model for Ordered Categorical Data
• Bayes Factors
• A Random-Effects Ordinal Regression Model for Multilevel 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
• Latent Variable Models for Clustered Ordinal Data
• Inference about Variance Components in the One-Way Model
• A Random Effects Model for Binary Data

This page was built for publication: Analysis of clustered ordinal data with subclusters via a bayesian hierarchical model