Evidence map›Paper›PMID 33616173›Full record

ArticleBiostatistics (Oxford, England)2022

Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data.

Tien Vo, Akshay Mishra, Vamsi Ithapu, Vikas Singh, Michael A Newton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biostatistics (Oxford, England), 2022. 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. Understanding Uncertainty Maps in Vision with Statistical Testing.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2022
    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

5 authors.

Tien VoDepartment of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.
Akshay MishraDepartment of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.
Vamsi IthapuDepartment of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.
Vikas SinghDepartment of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.
Michael A NewtonDepartment of Biostatistics and Medical Informatics, University of Wisconsin at Madison 610 Walnut Street, Madison, WI, USA.

Funding

TrainingU54AI117924 · NIAID · UNIVERSITY OF WISCONSIN-MADISON · PI CRAVEN, MARK W. · 2014 to 2018
$11.3M
Maximizing Power in AD Clinical Trials via Multimodal Machine LearningR01AG040396 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI SINGH, VIKAS · 2012 to 2015
$1.1M
NIAID NIH HHS U54 AI117924NIA NIH HHS R01 AG040396
6 · The paper itself

Abstract

For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false-discovery rates (FDRs). Compared to procedures that ignore the graph, the proposed Graph-based Mixture Model (GraphMM) method gains power in settings where non-null cases form connected subgraphs, and it does so by regularizing parameter contrasts between testing units. Simulations show that GraphMM controls the FDR in a variety of settings, though it may lose control with excessive regularization. On magnetic resonance imaging data from a study of brain changes associated with the onset of Alzheimer's disease, GraphMM produces greater yield than conventional large-scale testing procedures.

Indexed as

BrainMagnetic Resonance ImagingBayes TheoremHumansResearch DesignEmpirical BayesGraphMMGraph-respecting partitionImage analysisLocal false-discovery rateMixture model

Identifiers

PMID33616173
PMCPMC9295049

What OpenQuestion holds

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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.