A computationally efficient method for nonlinear mixed-effects models with nonignorable missing data in time-varying covariates

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A computationally efficient method for nonlinear mixed-effects models with nonignorable missing data in time-varying covariates

TitleA computationally efficient method for nonlinear mixed-effects models with nonignorable missing data in time-varying covariates
Publication TypeJournal Article
Year of Publication2007
AuthorsWU, LANG
JournalComputational Statistics & Data Analysis
Volume51
Pagination2410–2419
ISSN0167-9473
KeywordsEM algorithm, Linearization, Longitudinal data, Random effects model
AbstractNonlinear mixed-effects (NLME) models are widely used for longitudinal data analyses. Time-dependent covariates are often introduced to partially explain inter-individual variation. These covariates often have missing data, and the missingness may be nonignorable. Likelihood inference for NLME models with nonignorable missing data in time-varying covariates can be computationally very intensive and may even offer computational difficulties such as nonconvergence. We propose a computationally very efficient method for approximate likelihood inference. The method is illustrated using a real data example.
URLhttp://www.sciencedirect.com/science/article/pii/S0167947306002556
DOI10.1016/j.csda.2006.07.036