Bulletin of Mathematical Biology

, Volume 69, Issue 2, pp 563–584

Estimation and Prediction With HIV-Treatment Interruption Data

Authors

  • B. M. Adams
    • Center for Research in Scientific ComputationNorth Carolina State University
    • Center for Research in Scientific ComputationNorth Carolina State University
  • M. Davidian
    • Center for Research in Scientific ComputationNorth Carolina State University
  • E. S. Rosenberg
    • I.D. Unit—Gray 5Massachusetts General Hospital and Harvard Medical School
Original Article

DOI: 10.1007/s11538-006-9140-6

Cite this article as:
Adams, B.M., Banks, H.T., Davidian, M. et al. Bull. Math. Biol. (2007) 69: 563. doi:10.1007/s11538-006-9140-6

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.

Keywords

HIV modelsTreatment interruptionsCensored dataParameter estimationPrediction
Download to read the full article text

Copyright information

© Society for Mathematical Biology 2006