@article {wu_flexible_2002,
	title = {Flexible Weighted Log-Rank Tests Optimal for Detecting Early and/or Late Survival Differences},
	journal = {Biometrics},
	volume = {58},
	number = {4},
	year = {2002},
	month = {dec},
	pages = {997{\textendash}1004},
	abstract = {Summary. At the present time, many AIDS clinical trials compare drug therapies by a time-to-event primary endpoint that measures the durability of suppression of HIV replication. For such studies, survival differences tend to occur early and/or late in the follow-up period due to drug differences in initial potency and/or durability of efficacy, and detecting these differences is of primary interest. We propose a weighted log-rank statistic that emphasizes early and/or late survival differences. We also consider some versatile tests that also emphasize these differences but are sensitive to a wider range of alternatives. The performances of the new tests are evaluated in numerical studies. For the alternatives of interest, the new tests show greater power and flexibility than commonly used weighted log-rank tests and related versatile tests. When the main interest is in detecting early and/or late survival differences, these tests may be preferable to the other versatile and weighted log-rank tests that have been studied.},
	keywords = {AIDS, Asymptotics, Quadratic weight, Robust test, Versatile test},
	issn = {1541-0420},
	doi = {10.1111/j.0006-341X.2002.00997.x},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.0006-341X.2002.00997.x/abstract},
	author = {WU, LANG and Gilbert, Peter B.}
}
@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}
}
