Evidence map›Paper›PMID 38919945›Full record

ArticleProceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...)2021

Stochastic Version of EM Algorithm for Nonlinear Random Change-Point Models.

Hongbin Zhang, Binod Manandhar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...), 2021. 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
–field-weighted citation impact
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

2 authors.

Hongbin ZhangDepartment of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, 55 West 125th Street, New York, United States.
Binod ManandharInstitute of Implementation Science for Population Health, City University of New York, 55 West 125th Street, New York, United States.

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

Random effect change-point models are commonly used to infer individual-specific time of event that induces trend change of longitudinal data. Linear models are often employed before and after the change point. However, in applications such as HIV studies, a mechanistic nonlinear model can be derived for the process based on the underlying data-generation mechanisms and such nonlinear model may provide better ``predictions". In this article, we propose a random change-point model in which we model the longitudinal data by segmented nonlinear mixed effect models. Inference wise, we propose a maximum likelihood solution where we use the Stochastic Expectation-Maximization (StEM) algorithm coupled with independent multivariate rejection sampling through Gibbs's sampler. We evaluate the method with simulations to gain insights.

Indexed as

Gibbs’s samplerMultivariate rejection samplingNonlinear mixed effects modelRandom change-point modelStochastic version of EM

Identifiers

PMID38919945
PMCPMC11198015

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.