Evidence map›Paper›PMID 34433846›Full record

ArticleScientific reports2021

Hierarchical Bayesian estimation of covariate effects on airway and alveolar nitric oxide.

Jingying Weng, Noa Molshatzki, Paul Marjoram, W James Gauderman, Frank D Gilliland, Sandrah P Eckel

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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
0.3field-weighted citation impact, top 48% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  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 at 1 institution in 1 country.

Jingying WengDepartment of Population and Public Health Sciences, University of Southern California, 2001 N. Soto Street, SSB 202B, MC-9234, Los Angeles, CA, 90089, USA.
Noa MolshatzkiDepartment of Population and Public Health Sciences, University of Southern California, 2001 N. Soto Street, SSB 202B, MC-9234, Los Angeles, CA, 90089, USA.
Paul MarjoramDepartment of Population and Public Health Sciences, University of Southern California, 2001 N. Soto Street, SSB 202B, MC-9234, Los Angeles, CA, 90089, USA.
W James GaudermanDepartment of Population and Public Health Sciences, University of Southern California, 2001 N. Soto Street, SSB 202B, MC-9234, Los Angeles, CA, 90089, USA.
Frank D GillilandDepartment of Population and Public Health Sciences, University of Southern California, 2001 N. Soto Street, SSB 202B, MC-9234, Los Angeles, CA, 90089, USA.
Sandrah P EckelDepartment of Population and Public Health Sciences, University of Southern California, 2001 N. Soto Street, SSB 202B, MC-9234, Los Angeles, CA, 90089, USA. eckel@usc.edu.
University of Southern California · US

Funding

Translational Research Support CoreP30ES007048 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ROB S MCCONNELL · 1996 to 2026
$46.4M
Early detection of adverse respiratory effects from air pollutionR01ES023262 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BERHANE, KIROS T, GILLILAND, FRANK D. · 2013 to 2016
$2.0M
Statistical methods for exhaled breath biomarkers in environmental epidemiologyR01ES027860 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ECKEL, SANDRAH PROCTOR · 2017 to 2019
$1.1M
NIEHS NIH HHS P30 ES007048NIEHS NIH HHS R01 ES023262NIEHS NIH HHS R01 ES027860NIEHS NIH HHS R01ES027860
6 · The paper itself

Abstract

Exhaled breath biomarkers are an important emerging field. The fractional concentration of exhaled nitric oxide (FeNO) is a marker of airway inflammation with clinical and epidemiological applications (e.g., air pollution health effects studies). Systems of differential equations describe FeNO-measured non-invasively at the mouth-as a function of exhalation flow rate and parameters representing airway and alveolar sources of NO in the airway. Traditionally, NO parameters have been estimated separately for each study participant (Stage I) and then related to covariates (Stage II). Statistical properties of these two-step approaches have not been investigated. In simulation studies, we evaluated finite sample properties of existing two-step methods as well as a novel Unified Hierarchical Bayesian (U-HB) model. The U-HB is a one-step estimation method developed with the goal of properly propagating uncertainty as well as increasing power and reducing type I error for estimating associations of covariates with NO parameters. We demonstrated the U-HB method in an analysis of data from the southern California Children's Health Study relating traffic-related air pollution exposure to airway and alveolar airway inflammation.

Indexed as

ExhalationModels, TheoreticalAsthmaBayes TheoremBiomarkersBreath TestsChildData Interpretation, StatisticalHumansInhalation ExposureNitric OxidePulmonary AlveoliRespiratory MucosaVehicle EmissionsBiomarkersNitric OxideVehicle Emissions

Identifiers

PMID34433846
PMCPMC8387480
OpenAlexW3195871562

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.