A refined parameter estimating approach for HIV dynamic model

Tao Lu, Yangxin Huang, Min Wang, Feng Qian

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


HIV dynamic models, a set of ordinary differential equations (ODEs), have provided new understanding of the pathogenesis of HIV infection and the treatment effects of antiviral therapies. However, to estimate parameters for ODEs is very challenging due to the complexity of this nonlinear system. In this article, we propose a comprehensive procedure to deal with this issue. In the proposed procedure, a series of cutting-edge statistical methods and techniques are employed, including nonparametric mixed-effects smoothing-based methods for ODE models and stochastic approximation expectation-maximization (EM) approach for mixed-effects ODE models. A simulation study is performed to validate the proposed approach. An application example from a real HIV clinical trial study is used to illustrate the usefulness of the proposed method.

Original languageEnglish
Pages (from-to)1645-1657
Number of pages13
JournalJournal of Applied Statistics
Issue number8
StatePublished - Aug 2014


  • dynamic model
  • nonlinear mixed-effects model
  • nonparametric mixed-effects model; SAEM


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