Accounting for drop-out using inverse probability censoring weights in longitudinal clustered data with informative cluster size

DOI10.1214/21-AOAS1518zbMath1498.62244OpenAlexW4221109862MaRDI QIDQ2135387

Elizabeth K. Kaye, Aya A. Mitani, Kerrie P. Nelson

Publication date: 6 May 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/21-aoas1518

zbMATH Keywords

longitudinal dataclustered datageneralized estimating equationsperiodontal diseasetooth loss

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Cites Work

• Marginal Analyses of Clustered Data When Cluster Size Is Informative
• A generalized linear mixed model for longitudinal binary data with a marginal logit link function
• An alternative approach to the analysis of longitudinal data via generalized estimating equations
• Within-cluster resampling
• A Weighting Approach for GEE Analysis with Missing Data
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Inference for marginal linear models for clustered longitudinal data with potentially informative cluster sizes
• Marginal analysis of ordinal clustered longitudinal data with informative cluster size
• Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes
• Cluster adjusted regression for displaced subject data (CARDS): Marginal inference under potentially informative temporal cluster size profiles
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