Estimation and prediction with HIV-treatment interruption data.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Adams, BM; Banks, HT; Davidian, M; Rosenberg, ES

Published Date

  • February 2007

Published In

Volume / Issue

  • 69 / 2

Start / End Page

  • 563 - 584

PubMed ID

  • 17211735

International Standard Serial Number (ISSN)

  • 0092-8240

Digital Object Identifier (DOI)

  • 10.1007/s11538-006-9140-6


  • eng

Conference Location

  • United States