Mixed-Effects Models with Incomplete Data (Hardcover) ~ Lang Wu ... Cover Art

Mixed-Effects Models with Incomplete Data (Hardcover)

By: Lang Wu (Author)


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In longitudinal studies, incomplete data problems are almost inevitable as subjects may dropout or miss scheduled visits, covariates may be missing or measured with errors, and responses or covariates may be censored. This book reviews incomplete data problems in mixed-effects models for longitudinal studies and discusses various approaches, including some commonly used simple methods, EM algorithms, and multiple imputation methods. Other models for longitudinal data such as marginal models with missing values are also discussed. The book includes lots of detailed worked examples using real data and advice on how to implement the methods using software that can be downloaded from the web along with datasets.


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