Evidence map›Paper›PMID 31997385›Full record

ArticleStatistics in medicine2020

Methods for generalized change-point models: with applications to human immunodeficiency virus surveillance and diabetes data.

Jean de Dieu Tapsoba, Ching-Yun Wang, Sahar Zangeneh, Ying Qing Chen

Open access · greenAbstract read
In one paragraph

Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 30% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
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

4 authors at 2 institutions in 1 country.

Jean de Dieu TapsobaVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.ORCID 0000-0001-7453-4329
Ching-Yun WangDivision of Public Health, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Sahar ZangenehVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Ying Qing ChenVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Infectious Disease Research Institute · USFred Hutch Cancer Center · US

Funding

SDMC: HPTN 084 Pregnancy SupplementUM1AI068617 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Elizabeth Renata Brown, Deborah J Donnell · 2011 to 2026
$204.9M
Statistical Methods for Combination HIV Prevention ApproachesR01MH105857 · NIMH · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING · 2014 to 2018
$2.4M
Methods for Measurement Error in Physical Activity & Diet DataR21HL121347 · NHLBI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI WANG, CHING-YUN · 2014 to 2015
$491k
Novel Methods for Missing Subtype Data in Colorectal CancerR03CA235122 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI WANG, CHING-YUN · 2019 to 2020
$176k
NCI NIH HHS R03 CA235122NHLBI NIH HHS R21 HL121347NIAID NIH HHS UM1 AI068617NIMH NIH HHS R01 MH105857
6 · The paper itself

Abstract

In many epidemiological and biomedical studies, the association between a response variable and some covariates of interest may change at one or several thresholds of the covariates. Change-point models are suitable for investigating the relationship between the response and covariates in such situations. We present change-point models, with at least one unknown change-point occurring with respect to some covariates of a generalized linear model for independent or correlated data. We develop methods for the estimation of the model parameters and investigate their finite-sample performances in simulations. We apply the proposed methods to examine the trends in the reported estimates of the annual percentage of new human immunodeficiency virus (HIV) diagnoses linked to HIV-related medical care within 3 months after diagnosis using HIV surveillance data from the HIV prevention trial network 065 study. We also apply our methods to a dataset from the Pima Indian diabetes study to examine the effects of age and body mass index on the risk of being diagnosed with type 2 diabetes.

Indexed as

Diabetes Mellitus, Type 2HIV InfectionsBody Mass IndexHIVHumansLinear Modelschange-pointcorrelated datageneralized linear modelHIV surveillancetype 2 diabetes

Identifiers

PMID31997385
PMCPMC7260994
OpenAlexW3004146904

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.