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

Wei Liu and Lang Wu

Journal of Applied Statistics, 2012, vol. 39, issue 5, 963-978

Abstract: HIV 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.

Date: 2012
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DOI: 10.1080/02664763.2011.632404

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