@article {wu_computationally_2007,
	title = {A computationally efficient method for nonlinear mixed-effects models with nonignorable missing data in time-varying covariates},
	journal = {Computational Statistics \& Data Analysis},
	volume = {51},
	number = {5},
	year = {2007},
	pages = {2410{\textendash}2419},
	abstract = {Nonlinear 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.},
	keywords = {EM algorithm, Linearization, Longitudinal data, Random effects model},
	issn = {0167-9473},
	doi = {10.1016/j.csda.2006.07.036},
	url = {http://www.sciencedirect.com/science/article/pii/S0167947306002556},
	author = {WU, LANG}
}
