@article {yi_simultaneous_2011,
	title = {Simultaneous Inference and Bias Analysis for Longitudinal Data with Covariate Measurement Error and Missing Responses},
	journal = {Biometrics},
	volume = {67},
	number = {1},
	year = {2011},
	month = {mar},
	pages = {67{\textendash}75},
	abstract = {Summary Longitudinal data arise frequently in medical studies and it is common practice to analyze such data with generalized linear mixed models. Such models enable us to account for various types of heterogeneity, including between- and within-subjects ones. Inferential procedures complicate dramatically when missing observations or measurement error arise. In the literature, there has been considerable interest in accommodating either incompleteness or covariate measurement error under random effects models. However, there is relatively little work concerning both features simultaneously. There is a need to fill up this gap as longitudinal data do often have both characteristics. In this article, our objectives are to study simultaneous impact of missingness and covariate measurement error on inferential procedures and to develop a valid method that is both computationally feasible and theoretically valid. Simulation studies are conducted to assess the performance of the proposed method, and a real example is analyzed with the proposed method.},
	keywords = {Bias analysis, Longitudinal data, Measurement error, missing data, Monte Carlo EM algorithm, Random effects models},
	issn = {1541-0420},
	doi = {10.1111/j.1541-0420.2010.01437.x},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2010.01437.x/abstract},
	author = {Yi, G. Y. and Liu, W. and WU, LANG}
}
@article {liu_asymptotic_2010,
	title = {Some asymptotic results for semiparametric nonlinear mixed-effects models with incomplete data},
	journal = {Journal of Statistical Planning and Inference},
	volume = {140},
	number = {1},
	year = {2010},
	month = {jan},
	pages = {52{\textendash}64},
	abstract = {In modeling complex longitudinal data, semiparametric nonlinear mixed-effects (SNLME) models are very flexible and useful. Covariates are often introduced in the models to partially explain the inter-individual variations. In practice, data are often incomplete in the sense that there are often measurement errors and missing data in longitudinal studies. The likelihood method is a standard approach for inference for these models but it can be computationally very challenging, so computationally efficient approximate methods are quite valuable. However, the performance of these approximate methods is often based on limited simulation studies, and theoretical results are unavailable for many approximate methods. In this article, we consider a computationally efficient approximate method for a class of SNLME models with incomplete data and investigate its theoretical properties. We show that the estimates based on the approximate method are consistent and asymptotically normally distributed.},
	keywords = {Approximation, Asymptotics, Longitudinal data, Measurement error},
	issn = {0378-3758},
	doi = {10.1016/j.jspi.2009.06.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0378375809001888},
	author = {Liu, Wei and WU, LANG}
}
