@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} }