Evidence map›Paper›PMID 41907302›Full record

ArticleAlgorithms2025

Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring.

Hongbin Zhang, McKaylee Robertson, Sarah L Braunstein, David B Hanna, Uriel R Felsen, Levi Waldron, Denis Nash

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Algorithms, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hongbin ZhangDepartment of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY 40506, USA.
McKaylee RobertsonInstitute for Implementation Science in Population Health, City University of New York, New York, NY 10027, USA.
Sarah L BraunsteinBureau of Hepatitis, HIV, and Sexually Transmitted Infections, Department of Health and Mental Hygiene, New York, NY, USA.
David B HannaDepartment of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
Uriel R FelsenDepartment of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USA.
Levi WaldronInstitute for Implementation Science in Population Health, City University of New York, New York, NY 10027, USA.
Denis NashInstitute for Implementation Science in Population Health, City University of New York, New York, NY 10027, USA.

Funding

Obtaining population-based estimates of the timing of antiretroviral therapy initiation using HIV surveillance data: Change-point model development, validation, and disseminationR21AI147933 · NIAID · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI NASH, DENIS, ZHANG, HONGBIN · 2020 to 2021
$446k
NIAID NIH HHS R21 AI147933
6 · The paper itself

Abstract

We propose a new random change point model that utilizes routinely recorded individual-level HIV viral load data to estimate the timing of antiretroviral therapy (ART) initiation in people living with HIV. The change point distribution is assumed to follow a zero-inflated exponential distribution for the longitudinal data, which is also subject to left-censoring, and the underlying data-generating mechanism is a nonlinear mixed-effects model. We extend the Stochastic EM (StEM) algorithm by combining a Gibbs sampler with a Metropolis-Hastings sampling. We apply the method to real HIV data to infer the timing of ART initiation since diagnosis. Additionally, we conduct simulation studies to assess the performance of our proposed method.

Indexed as

antiretroviral therapycensored dataGibbs samplerMetropolis–Hastings samplingnonlinear mixed-effects modelrandom change point modelStochastic EMzero-inflated exponential distribution

Identifiers

PMID41907302
PMCPMC13021155

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.