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• W2140949017 abstract "In longitudinal studies investigators frequently have to assess and address potential biases introduced by missing data. New methods are proposed for modeling longitudinal categorical data with nonignorable dropout using marginalized transition models and shared random effects models. Random effects are introduced for both serial dependence of outcomes and nonignorable missingness. Fisher-scoring and Quasi–Newton algorithms are developed for parameter estimation. Methods are illustrated with a real dataset." @default.
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• W2140949017 date "2014-01-15" @default.
• W2140949017 modified "2023-09-24" @default.
• W2140949017 title "Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout" @default.
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• W2140949017 doi "https://doi.org/10.1002/bimj.201200085" @default.
• W2140949017 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24430985" @default.
• W2140949017 hasPublicationYear "2014" @default.
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