Inference for marginal linear models for clustered longitudinal data with potentially informative cluster sizes

DOI10.1177/0962280209347043zbMath1272.62098OpenAlexW2126377029WikidataQ33540102 ScholiaQ33540102MaRDI QIDQ4924355

Somnath Datta, Ming Wang, Maiying Kong

Publication date: 31 May 2013

Published in: Statistical Methods in Medical Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1177/0962280209347043

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (11)

Accounting for drop-out using inverse probability censoring weights in longitudinal clustered data with informative cluster size ⋮ Fast, closed-form, and efficient estimators for hierarchical models with AR(1) covariance and unequal cluster sizes ⋮ Multivariate linear mixed models with censored and nonignorable missing outcomes, with application to AIDS studies ⋮ A Tutorial on the Practical Use and Implication of Complete Sufficient Statistics ⋮ Semiparametric marginal methods for clustered data adjusting for informative cluster size with nonignorable zeros ⋮ Marginal analysis of multiple outcomes with informative cluster size ⋮ A note on misspecification in joint modeling of correlated data with informative cluster sizes ⋮ Inference on the marginal distribution of clustered data with informative cluster size ⋮ Methods for observed‐cluster inference when cluster size is informative: A review and clarifications ⋮ Cluster adjusted regression for displaced subject data (CARDS): Marginal inference under potentially informative temporal cluster size profiles ⋮ Robust estimation of marginal regression parameters in clustered data

Cites Work

• Longitudinal data analysis using generalized linear models
• Marginal Analyses of Clustered Data When Cluster Size Is Informative
• On eliminating the asymptotic bias in the quasi-least squares estimate of the correlation parameter.
• Within-cluster resampling
• Multiple Outputation: Inference for Complex Clustered Data by Averaging Analyses from Independent Data
• Models for Longitudinal Data: A Generalized Estimating Equation Approach
• On the use of a working correlation matrix in using generalised linear models for repeated measures
• Marginal Analysis of Correlated Failure Time Data with Informative Cluster Sizes
• Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes

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