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Link to original content: https://doi.org/10.1007/s11538-006-9140-6
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Estimation and Prediction With HIV-Treatment Interruption Data

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Abstract

We consider longitudinal clinical data for HIV patients undergoing treatment interruptions. We use a nonlinear dynamical mathematical model in attempts to fit individual patient data. A statistically-based censored data method is combined with inverse problem techniques to estimate dynamic parameters. The predictive capabilities of this approach are demonstrated by comparing simulations based on estimation of parameters using only half of the longitudinal observations to the full longitudinal data sets.

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Authors and Affiliations

  1. Center for Research in Scientific Computation, North Carolina State University, Box 8205, Raleigh, NC, 27695-8205, USA

    B. M. Adams, H. T. Banks & M. Davidian

  2. I.D. Unit—Gray 5, Massachusetts General Hospital and Harvard Medical School, Fruit Street, Boston, MA, 02114, USA

    E. S. Rosenberg

Authors
  1. B. M. Adams
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  2. H. T. Banks
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  3. M. Davidian
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  4. E. S. Rosenberg
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Corresponding author

Correspondence to H. T. Banks.

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Adams, B.M., Banks, H.T., Davidian, M. et al. Estimation and Prediction With HIV-Treatment Interruption Data. Bull. Math. Biol. 69, 563–584 (2007). https://doi.org/10.1007/s11538-006-9140-6

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  • DOI: https://doi.org/10.1007/s11538-006-9140-6

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