@article {wu_simultaneous_2004,
	title = {Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies},
	journal = {Statistics in Medicine},
	volume = {23},
	number = {11},
	year = {2004},
	month = {jun},
	pages = {1715{\textendash}1731},
	abstract = {In AIDS studies such as HIV viral dynamics, statistical inference is often complicated because the viral load measurements may be subject to left censoring due to a detection limit and time-varying covariates such as CD4 counts may be measured with substantial errors. Mixed-effects models are often used to model the response and the covariate processes in these studies. We propose a unified approach which addresses the censoring and measurement errors simultaneously. We estimate the model parameters by a Monte-Carlo EM algorithm via the Gibbs sampler. A simulation study is conducted to compare the proposed method with the usual two-step method and a naive method. We find that the proposed method produces approximately unbiased estimates with more reliable standard errors. A real data set from an AIDS study is analysed using the proposed method. Copyright {\textcopyright} 2004 John Wiley \& Sons, Ltd.},
	keywords = {EM algorithm, Gibbs sampler, HIV, mixed-effects model},
	issn = {1097-0258},
	doi = {10.1002/sim.1748},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/sim.1748/abstract},
	author = {WU, LANG}
}
@article {wu_identification_2002,
	title = {Identification of significant host factors for HIV dynamics modelled by non-linear mixed-effects models},
	journal = {Statistics in Medicine},
	volume = {21},
	number = {5},
	year = {2002},
	month = {mar},
	pages = {753{\textendash}771},
	abstract = {Non-linear mixed-effects models are powerful tools for modelling HIV viral dynamics. In AIDS clinical trials, the viral load measurements for each subject are often sparse. In such cases, linearization procedures are usually used for inferences. Under such linearization procedures, however, standard covariate selection methods based on the approximate likelihood, such as the likelihood ratio test, may not be reliable. In order to identify significant host factors for HIV dynamics, in this paper we consider two alternative approaches for covariate selection: one is based on individual non-linear least square estimates and the other is based on individual empirical Bayes estimates. Our simulation study shows that, if the within-individual data are sparse and the between-individual variation is large, the two alternative covariate selection methods are more reliable than the likelihood ratio test, and the more powerful method based on individual empirical Bayes estimates is especially preferable. We also consider the missing data in covariates. The commonly used missing data methods may lead to misleading results. We recommend a multiple imputation method to handle missing covariates. A real data set from an AIDS clinical trial is analysed based on various covariate selection methods and missing data methods. Copyright {\textcopyright} 2002 John Wiley \& Sons, Ltd.},
	keywords = {AIDS, HIV, HIV dynamics, missing data, mixed-effects models, Multiple imputation},
	issn = {1097-0258},
	doi = {10.1002/sim.1015},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/sim.1015/abstract},
	author = {Wu, Hulin and WU, LANG}
}
