@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}
}
@conference { ISI:000259327700030,
	title = {Fast robust variable selection},
	booktitle = {COMPSTAT 2008: PROCEEDINGS IN COMPUTATIONAL STATISTICS},
	year = {2008},
	note = {18th Symposium on Computational Statistics (COMSTAT 2008), Oporto, PORTUGAL, AUG 24-29, 2008},
	pages = {359-370},
	publisher = {Univ Porto, Fac Econ; PSE; FCT; FEUP; Banco Porutgal; PORTO; SPM; Caixa Geral Depositos; SPE; CLAD; ifcs},
	organization = {Univ Porto, Fac Econ; PSE; FCT; FEUP; Banco Porutgal; PORTO; SPM; Caixa Geral Depositos; SPE; CLAD; ifcs},
	type = {Proceedings Paper},
	address = {TIERGARTENSTR 17, D-69121 HEIDELBERG, GERMANY},
	abstract = {We discuss some computationally efficient procedures for robust variable selection in linear regression. A key component in these procedures is the computation of robust correlations between pairs of variables. We show that the robust variable selection procedures can easily handle missing data under the assumption that data are missing completely at random.},
	keywords = {correlation, missing data, Robustness, variable selection},
	isbn = {978-3-7908-2083-6},
	author = {Van Aelst, Stefan and Khan, Jafar A. and Zamar, Ruben H.},
	editor = {Brito, P}
}
@article {wu_hiv_2007,
	title = {HIV viral dynamic models with dropouts and missing covariates},
	journal = {Statistics in Medicine},
	volume = {26},
	number = {17},
	year = {2007},
	pages = {3342{\textendash}3357},
	abstract = {In recent years HIV viral dynamic models have received great attention in AIDS studies. Often, subjects in these studies may drop out for various reasons such as drug intolerance or drug resistance, and covariates may also contain missing data. Statistical analyses ignoring informative dropouts and missing covariates may lead to misleading results. We consider appropriate methods for HIV viral dynamic models with informative dropouts and missing covariates and evaluate these methods via simulations. A real data set is analysed, and the results show that the initial viral decay rate, which may reflect the efficacy of the anti-HIV treatment, may be over-estimated if dropout patients are ignored. We also find that the current or immediate previous viral load values may be most predictive for patients{\textquoteright} dropout. These results may be important for HIV/AIDS studies. Copyright {\textcopyright} 2007 John Wiley \& Sons, Ltd.},
	keywords = {approximate method, Longitudinal data, missing data, Monte-Carlo EM, nonlinear mixed-effects model},
	issn = {1097-0258},
	doi = {10.1002/sim.2816},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/sim.2816/abstract},
	author = {WU, LANG}
}
@article {wu_nonlinear_2004,
	title = {Nonlinear mixed-effect models with nonignorably missing covariates},
	journal = {Canadian Journal of Statistics},
	volume = {32},
	number = {1},
	year = {2004},
	month = {mar},
	pages = {27{\textendash}37},
	abstract = {Nonlinear mixed-effect models are often used in the analysis of longitudinal data. However, it sometimes happens that missing values for some of the model covariates are not purely random. Motivated by an application to HTV viral dynamics, where this situation occurs, the author considers likelihood inference for this type of problem. His approach involves a Monte Carlo EM algorithm, along with a Gibbs sampler and rejection/importance sampling methods. A concrete application is provided.},
	keywords = {EM algorithm, Gibbs sampling, Longitudinal data, missing data, Rejection sampling},
	issn = {1708-945X},
	doi = {10.2307/3315997},
	url = {http://onlinelibrary.wiley.com/doi/10.2307/3315997/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}
}
@article {wu_missing_2002,
	title = {Missing time-dependent covariates in human immunodeficiency virus dynamic models},
	journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
	volume = {51},
	number = {3},
	year = {2002},
	month = {jul},
	pages = {297{\textendash}318},
	abstract = {Summary. The study of human immunodeficiency virus dynamics is one of the most important areas in research into acquired immune deficiency syndrome in recent years. Non-linear mixed effects models have been proposed for modelling viral dynamic processes. A challenging problem in the modelling is to identify repeatedly measured (time-dependent), but possibly missing, immunologic or virologic markers (covariates) for viral dynamic parameters. For missing time-dependent covariates in non-linear mixed effects models, the commonly used complete-case, mean imputation and last value carried forward methods may give misleading results. We propose a three-step hierarchical multiple-imputation method, implemented by Gibbs sampling, which imputes the missing data at the individual level but can pool information across individuals. We compare various methods by Monte Carlo simulations and find that the multiple-imputation method proposed performs the best in terms of bias and mean-squared errors in the estimates of covariate coefficients. By applying the favoured multiple-imputation method to clinical data, we conclude that there is a negative correlation between the viral decay rate (a virological response parameter) and CD4 or CD8 cell counts during the treatment; this is counter-intuitive, but biologically interpretable on the basis of findings from other clinical studies. These results may have an important influence on decisions about treatment for acquired immune deficiency syndrome patients.},
	keywords = {Acquired immune deficiency syndrome, Gibbs sampler, Hierarchical model, Human immunodeficiency virus dynamics, Markov chain Monte Carlo methods, missing data, Multiple imputation, Non-linear mixed effects model},
	issn = {1467-9876},
	doi = {10.1111/1467-9876.00270},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/1467-9876.00270/abstract},
	author = {WU, LANG and Wu, Hulin}
}
