Files and links (1)
Accepted (AM)Open Access (License Unspecified), Open
Journal article
Segmental modeling of changing viral load to assess drug resistance in HIV infection
Statistical methods in medical research, v 16(4), pp 365-373
Aug 2007
PMID: 17715162
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Studies of viral dynamics are important to our understanding of the pathogenesis of HIV-1 infection and in assessment of the potency of antiretroviral therapies. Although many different viral dynamic models and methods for estimating viral dynamic parameters have been proposed and used in various studies, none has been entirely satisfactory. We propose here a segmental model to describe the viral load, and estimate the dynamic parameters by using a mixed-effects model. We address the relation between the baseline viral load and the decay rate of the first phase of viral load decay, and divide patients into three categories on the basis of their changing viral load patterns.
Metrics
Details
- Title
- Segmental modeling of changing viral load to assess drug resistance in HIV infection
- Creators
- H Liang - University of Rochester Medical Center
- Publication Details
- Statistical methods in medical research, v 16(4), pp 365-373
- Publisher
- Sage
- Grant note
- R01 AI062247 / NIAID NIH HHS AI50020 / NIAID NIH HHS R01 AI062247-02 / NIAID NIH HHS N01AI50020 / NIAID NIH HHS AI62247-01 / NIAID NIH HHS R01 AI059773 / NIAID NIH HHS R01 AI059773-02 / NIAID NIH HHS R01 AI062247-01 / NIAID NIH HHS AI59773 / NIAID NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000248753000004
- Scopus ID
- 2-s2.0-34547684609
- Other Identifier
- 991019320611004721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Health Care Sciences & Services
- Mathematical & Computational Biology
- Medical Informatics
- Statistics & Probability