Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Statistics > Methodology

arXiv:2204.00048 (stat)
[Submitted on 31 Mar 2022]

Title:Improving Biomarker Based HIV Incidence Estimation in the Treatment Era

Authors:Ian E. Fellows, Wolfgang Hladik, Jeffrey W. Eaton, Andrew C. Voetsch, Bharat S. Parekh, Ray W. Shiraishi
View PDF
Abstract:Estimating HIV-1 incidence using biomarker assays in cross-sectional surveys is important for understanding the HIV pandemic. However, the utility of these estimates has been limited by uncertainty about what input parameters to use for False Recency Rate (FRR) and Mean Duration of Recent Infection (MDRI) after applying recent infection testing algorithm (RITA). This article shows how testing and diagnosis in a population reduce both FRR and MDRI compared to a treatment-naive population. Using self-reported testing history, a new method is proposed for calculating appropriate context-specific estimates of FRR and MDRI. The result of this is a new formula for incidence that depends only on reference FRR and MDRI parameters derived in an undiagnosed, treatment-naive, non-elite controller, non-AIDS-progressed population.
Subjects: Methodology (stat.ME); Applications (stat.AP)
Cite as: arXiv:2204.00048 [stat.ME]
  (or arXiv:2204.00048v1 [stat.ME] for this version)
  https://doi.org/10.48550/arXiv.2204.00048

Submission history

From: Ian Fellows [view email]
[v1] Thu, 31 Mar 2022 19:00:45 UTC (443 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
stat.ME
< prev   |   next >
new | recent | 2022-04
Change to browse by:
stat
stat.AP

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)