Yale University

Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring.

TitleSuper Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring.
Publication TypeJournal Article
Year of Publication2015
AuthorsPetersen, 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
JournalJournal of acquired immune deficiency syndromes (1999)
Volume69
Issue1
Pagination109-18
Date Published2015 May 1
ISSN1944-7884
AbstractRegular 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.
DOI10.1097/QAI.0000000000000548
Alternate JournalJ. Acquir. Immune Defic. Syndr.

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