Covariate Decomposition Methods for Longitudinal Missing-at-Random Data and Predictors Associated with Subject-Specific Effects

DOI10.1111/ANZS.12093zbMath1336.62022OpenAlexW2152968567WikidataQ30967542 ScholiaQ30967542MaRDI QIDQ2804161

John M. Neuhaus, Charles E. McCulloch

Publication date: 28 April 2016

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4456042

zbMATH Keywords

biasconfoundingconditional likelihoodconsistent estimation

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (1)

On the effect of ignoring correlation in the covariates when fitting linear mixed models

Cites Work

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• Separating Between- and Within-Cluster Covariate Effects by Using Conditional and Partitioning Methods
• Between- and Within-Cluster Covariate Effects in the Analysis of Clustered Data
• Protective estimation of mixed-effects logistic regression when data are not missing at random
• Conditional Inference Methods for Incomplete Poisson Data With Endogenous Time-Varying Covariates

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