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Activity Number: 510
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #305492
Title: Parameter Estimation for HIV Dynamic Model by Mixed-Effects Models
Author(s): Yao Yu*+
Companies: University of Rochester
Address: Dept. of Biostatistics and Computational Biol, Rochester, NY, 14620,
Keywords: HIV dynamic model ; Nonlinear mixed-effects model ; Variance components ; Random-effects

We apply nonlinear mixed-effects models (NLME) to estimate the fixed-effects and random-effects for the parameters in Perelson's HIV dynamic model,a system of mechanism-based ordinary differential equations (ODE) and the baseline of infected CD4+ T cell counts. In this approach, we solve the ODE directly without making any model approximations or fixing any parameters to obtain close-form solutions, and the conclusion maintains the biological interpretability for dynamic parameters. Simulation studies are conducted, and the results demonstrate small biases of the fixed-effects estimates in settings where the observations for individual are rich as well as sparse. As to the estimation of variance components for the random-effects, settings considering the data without measurement errors provide us with good estimates. In addition, when obtaining a HIV clinic trial data set with small measurement errors is difficult, increasing the frequency of collecting measurements provides an alternative approach to efficiently improve the estimation of the variance components of random-effects. This approach is applied to a real data set collected from an AIDS clinical trial of HIV-1 study.

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