Evidence map›Paper›PMID 35707027›Full record

ArticleJournal of applied statistics2020

Identifying mediating variables with graphical models: an application to the study of causal pathways in people living with HIV.

Adrian Dobra, Katherine Buhikire, Joachim G Voss

Abstract read
In one paragraph

Article in Journal of applied statistics, 2020. 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

3 authors.

Adrian DobraDepartment of Statistics, University of Washington, Seattle, WA, USA.
Katherine BuhikireFrances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA.
Joachim G VossFrances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA.ORCID https://orcid.org/0000-0002-8072-6244

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We empirically demonstrate that graphical models can be a valuable tool in the identification of mediating variables in causal pathways. We make use of graphical models to elucidate the causal pathway through which the treatment influences the levels of fatigue and weakness in people living with HIV (PLHIV) based on a secondary analysis of a categorical dataset collected in a behavioral clinical trial: is weakness a mediator for the treatment and fatigue, or is fatigue a mediator for the treatment and weakness? Causal mediation analysis could not offer any definite answers to these questions.

Indexed as

Contingency tablesgraphical modelsHIVloglinear modelsmediation

Identifiers

PMID35707027
PMCPMC9042052

What OpenQuestion holds

Textmetadata
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.