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

Wei Liu and Lang Wu

Computational Statistics & Data Analysis, 2008, vol. 53, issue 1, 112-122

Abstract: Semiparametric 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.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2008:i:1:p:112-122

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