Evidence map›Paper›PMID 34393301›Full record

ArticleRevista Colombiana de estadistica2021

Bayesian Hierarchical Factor Analysis for Efficient Estimation across Race/Ethnicity.

Jinxiang Hu, Lauren Clark, Peng Shi, Vincent S Staggs, Christine Daley, Byron Gajewski

Abstract read
In one paragraph

Article in Revista Colombiana de estadistica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Jinxiang HuDepartment of Biostatistics & Data Science, University of Kansas Medical Center.
Lauren ClarkDepartment of Biostatistics & Data Science, University of Kansas Medical Center.
Peng ShiDepartment of Biostatistics & Data Science, University of Kansas Medical Center.
Vincent S StaggsBiostatistics & Epidemiology Core, Health Services & Outcomes Research, Children's Mercy Kansas City, and University of Missouri-Kansas City School of Medicine.
Christine DaleyDepartment of Family Medicine, University of Kansas Medical Center.
Byron GajewskiDepartment of Biostatistics & Data Science, University of Kansas Medical Center.

Funding

RADx-UP: Improving the Response of Local Urban and Rural Communities to Disparities in Covid-19 TestingUL1TR002366 · NCATS · UNIVERSITY OF KANSAS MEDICAL CENTER · PI Mario Castro, JAMES STEVEN LEEDER · 2017 to 2026
$44.3M
Transgenic & Gene-Targeting Shared ResourceP30CA168524 · NCI · UNIVERSITY OF KANSAS MEDICAL CENTER · PI ROY A. JENSEN · 2012 to 2026
$40.1M
Project 2P20MD004805 · NIMHD · UNIVERSITY OF KANSAS MEDICAL CENTER · PI DALEY, CHRISTINE MAKOSKY, GREINER, K ALLEN · 2010 to 2014
$7.6M
NCATS NIH HHS UL1 TR002366NCI NIH HHS P30 CA168524NIMHD NIH HHS P20 MD004805
6 · The paper itself

Abstract

Patient reported outcomes are gaining more attention in patient-centered health outcomes research and quality of life studies as important indicators of clinical outcomes, especially for patients with chronic diseases. Factor analysis is ideal for measuring patient reported outcomes. If there is heterogeneity in the patient population and when sample size is small, differential item functioning and convergence issues are challenges for applying factor models. Bayesian hierarchical factor analysis can assess health disparity by assessing for differential item functioning, while avoiding convergence problems. We conducted a simulation study and used an empirical example with American Indian minorities to show that fitting a Bayesian hierarchical factor model is an optimal solution regardless of heterogeneity of population and sample size.

Indexed as

American IndiansBayesian hierarchical modeldifferential item functioningfactor analysishealth disparitiespatient reported outcomes

Identifiers

PMID34393301
PMCPMC8356675

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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.