Methods for observed‐cluster inference when cluster size is informative: A review and clarifications

DOI10.1111/biom.12151zbMath1419.62438OpenAlexW1890313756WikidataQ35035067 ScholiaQ35035067MaRDI QIDQ5170217

Andrew Copas, Menelaos Pavlou, Shaun R. Seaman

Publication date: 22 July 2014

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/biom.12151

zbMATH Keywords

missing not at randombridge distributionsemi-continuous datamortal cohort inferenceinformative missingnessimmortal cohort inference

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 (7)

Nonparametric tests for multistate processes with clustered data ⋮ Non‐parametric regression in clustered multistate current status data with informative cluster size ⋮ Clinical risk prediction models and informative cluster size: Assessing the performance of a suicide risk prediction algorithm ⋮ Nonparametric analysis of nonhomogeneous multistate processes with clustered observations ⋮ A note on misspecification in joint modeling of correlated data with informative cluster sizes ⋮ Test for Informative Cluster Size with Right Censored Survival Data ⋮ Cluster adjusted regression for displaced subject data (CARDS): Marginal inference under potentially informative temporal cluster size profiles

Cites Work

• Matching conditional and marginal shapes in binary random intercept models using a bridge distribution function
• Marginal Analyses of Clustered Data When Cluster Size Is Informative
• What is meant by ``missing at random?
• Longitudinal data with follow-up truncated by death: match the analysis method to research aims
• Within-cluster resampling
• Likelihood Methods for Binary Responses of Present Components in a Cluster
• A Bayesian Approach for Joint Modeling of Cluster Size and Subunit-Specific Outcomes
• Informative Cluster Sizes for Subcluster-Level Covariates and Weighted Generalized Estimating Equations
• Estimation of covariate effects in generalized linear mixed models with informative cluster sizes
• On Confounding, Prediction and Efficiency in the Analysis of Longitudinal and Cross‐sectional Clustered 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
• Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes

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