An approximate method for nonlinear mixed-effects models with nonignorably missing covariates

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An approximate method for nonlinear mixed-effects models with nonignorably missing covariates

TitleAn approximate method for nonlinear mixed-effects models with nonignorably missing covariates
Publication TypeJournal Article
Year of Publication2008
AuthorsWU, LANG
JournalStatistics & Probability Letters
Volume78
Pagination384–389
ISSN0167-7152
KeywordsEM algorithm, Linearization, Longitudinal data, Taylor expansion
AbstractNonlinear mixed-effect (NLME) models are very useful in many longitudinal studies. In practice, covariates in NLME models may contain missing data, and the missing data may be nonignorable. Likelihood inference for NLME models with missing covariates can be computationally very intensive. We propose a computationally much more efficient approximate method for NLME models with nonignorably missing covariates. We illustrate the method using a real data example.
URLhttp://www.sciencedirect.com/science/article/pii/S0167715207002519
DOI10.1016/j.spl.2007.07.011