Two-step and likelihood methods for HIV viral dynamic models with covariate measurement errors and missing data

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Two-step and likelihood methods for HIV viral dynamic models with covariate measurement errors and missing data

TitleTwo-step and likelihood methods for HIV viral dynamic models with covariate measurement errors and missing data
Publication TypeJournal Article
Year of Publication2012
AuthorsLiu, W, WU, LANG
JournalJournal of Applied Statistics
Volume39
Pagination963–978
Date Publishedmay
ISSN0266-4763
AbstractHIV viral dynamic models have received much attention in the literature. Long-term viral dynamics may be modelled by semiparametric nonlinear mixed-effect models, which incorporate large variation between subjects and autocorrelation within subjects and are flexible in modelling complex viral load trajectories. Time-dependent covariates may be introduced in the dynamic models to partially explain the between-individual variations. In the presence of measurement errors and missing data in time-dependent covariates, we show that the commonly used two-step method may give approximately unbiased estimates but may under-estimate standard errors. We propose a two-stage bootstrap method to adjust the standard errors in the two-step method and a likelihood method.
URLhttp://dx.doi.org/10.1080/02664763.2011.632404
DOI10.1080/02664763.2011.632404