A semiparametric nonlinear mixed-effects model with non-ignorable missing data and measurement errors for HIV viral data

Subscribe to email list

Please select the email list(s) to which you wish to subscribe.

You are here

A semiparametric nonlinear mixed-effects model with non-ignorable missing data and measurement errors for HIV viral data

TitleA semiparametric nonlinear mixed-effects model with non-ignorable missing data and measurement errors for HIV viral data
Publication TypeJournal Article
Year of Publication2008
AuthorsLiu, W, WU, LANG
JournalComputational Statistics & Data Analysis
Volume53
Pagination112–122
Date Publishedsep
ISSN0167-9473
AbstractSemiparametric nonlinear mixed-effects (NLME) models are very flexible in modeling long-term HIV viral dynamics. In practice, statistical analyses are often complicated due to measurement errors and missing data in covariates and non-ignorable missing data in the responses. We consider likelihood methods which simultaneously address measurement error and missing data problems. A real dataset is analyzed in detail, and a simulation study is conducted to evaluate the methods.
URLhttp://www.sciencedirect.com/science/article/pii/S0167947308003290
DOI10.1016/j.csda.2008.06.018