Title | Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Petersen, Maya L., Erin LeDell, Joshua Schwab, Varada Sarovar, Robert Gross, Nancy Reynolds, Jessica E. Haberer, Kathy Goggin, Carol Golin, Julia Arnsten, Marc I. Rosen, Robert H. Remien, David Etoori, Ira B. Wilson, Jane M. Simoni, Judith A. Erlen, Mark J. van der Laan, Hong Hu Liu, and David R. Bangsberg |
Journal | Journal of acquired immune deficiency syndromes (1999) |
Volume | 69 |
Issue | 1 |
Pagination | 109-18 |
Date Published | 2015 May 1 |
ISSN | 1944-7884 |
Abstract | Regular HIV RNA testing for all HIV-positive patients on antiretroviral therapy (ART) is expensive and has low yield since most tests are undetectable. Selective testing of those at higher risk of failure may improve efficiency. We investigated whether a novel analysis of adherence data could correctly classify virological failure and potentially inform a selective testing strategy. |
DOI | 10.1097/QAI.0000000000000548 |
Alternate Journal | J. Acquir. Immune Defic. Syndr. |