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A Random-Effects Approach to Generalized Linear Mixed Model Analysis of Incomplete Longitudinal Data

We propose a random-effects approach to missing values for generalized linear mixed model (GLMM) analysis. The method converts a GLMM with missing covariates to another GLMM without missing covariates. The standard GLMM analysis tools for longitudinal data then apply. The method applies, in particular, to the cases of linear mixed models and logistic regression. Performance of the method is evaluated empirically, and compared with alternative approaches, including the popular MICE procedure of multiple imputation. Theoretical justification of the method is given, and explained, for the patterns observed in the simulation studies. Two real-data examples from healthcare studies are discussed.

preprint2025arXivOpen access
Thuan NguyenJiangshan ZhangJiming Jiang
Methodology
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Open access3 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Thuan NguyenJiangshan ZhangJiming Jiang

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