Evidence map›Paper›PMID 31206621›Full record

ArticleThe British journal of mathematical and statistical psychology2020

Back to the basics: Rethinking partial correlation network methodology.

Donald R Williams, Philippe Rast

Abstract read
In one paragraph

Article in The British journal of mathematical and statistical psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
88citing papers in PubMed, 2 pooled it
–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

88 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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28 more citing papers are in PubMed but not listed here.

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.

Donald R WilliamsUniversity of California, Davis, California, USA.ORCID 0000-0001-6735-8785
Philippe RastUniversity of California, Davis, California, USA.

Funding

A Multi-Study Replication of the Predictive Value of Intra-Individual Variabilityon Long-Term Changes in Cognition, Health and AffectR01AG050720 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI RAST, PHILIPPE · 2016 to 2018
$741k
National Academies of Sciences, Engineering, and Medicine FORD foundation pre-doctoral fellowshipNational Science Foundation Graduate Research FellowshipNIA NIH HHS R01 AG050720
6 · The paper itself

Abstract

The Gaussian graphical model (GGM) is an increasingly popular technique used in psychology to characterize relationships among observed variables. These relationships are represented as elements in the precision matrix. Standardizing the precision matrix and reversing the sign yields corresponding partial correlations that imply pairwise dependencies in which the effects of all other variables have been controlled for. The graphical lasso (glasso) has emerged as the default estimation method, which uses ℓ

Indexed as

Models, PsychologicalBayes TheoremBiostatisticsComputer SimulationConfidence IntervalsFalse Positive ReactionsHumansLikelihood FunctionsMarkov ChainsNormal DistributionProbabilityProof of Concept StudyPsychologyStress Disorders, Post-Traumaticconfidence intervalFisher Z-transformationGaussian graphical modelℓ1-regularizationmaximum likelihoodpartial correlation

Identifiers

PMID31206621
PMCPMC8572131

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.