Random Change-Point Non-linear Mixed Effects Model for left-censored longitudinal data: An application to HIV surveillance.

Binod Manandhar, Hongbin Zhang

Abstract read

In one paragraph

Article in Proceedings. American Statistical Association. Annual Meeting, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Binod ManandharCity University of New York, Graduate School of Public Health, 55 W 125th St,New York, NY 10027.

Hongbin ZhangCity University of New York, Graduate School of Public Health, 55 W 125th St,New York, NY 10027.

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

A change-point model is essential in longitudinal data to infer an individual specific time to an event that induces a change of trend. However, in general, change points are not known for population-based data. We present an unknown change-point model that fits the linear and non-linear mixed effects for pre- and post-change points. We address the left-censored observations. Through stochastic approximation expectation maximization (SAEM) with the Metropolis Hasting sampler, we fit a random change-point non-linear mixed effects model. We apply our method on the longitudinal viral load (VL) data reported to the HIV surveillance registry from New York City.

Indexed as

Antiretroviral therapyCensored dataChange-pointExpectation maximizationLongitudinal dataMetropolis-Hastings samplerMixed-effect modelStochastic approximation

Identifiers

PMID38855090

PMCPMC11162255

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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.