Evidence map›Paper›PMID 22522380›Full record

ArticleBiometrical journal. Biometrische Zeitschrift2012

Joint generalized models for multidimensional outcomes: a case study of neuroscience data from multimodalities.

Xiao-Feng Wang

Abstract read
In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. 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

1 author.

Xiao-Feng WangDepartment of Quantitative Health Sciences/Biostatistics Section, Cleveland Clinic Foundation, Cleveland, OH 44195, USA. wangx6@ccf.org

Funding

CTSA INFRASTRUCTURE FOR PEDIATRIC RESEARCHUL1RR024989 · NCRR · CASE WESTERN RESERVE UNIVERSITY · PI DAVIS, PAMELA B · 2007 to 2011
$53.7M
NCRR NIH HHS UL1 RR024989
6 · The paper itself

Abstract

This paper is motivated from the analysis of neuroscience data in a study of neural and muscular mechanisms of muscle fatigue. Multidimensional outcomes of different natures were obtained simultaneously from multiple modalities, including handgrip force, electromyography (EMG), and functional magnetic resonance imaging (fMRI). We first study individual modeling of the univariate response depending on its nature. A mixed-effects beta model and a mixed-effects simplex model are compared for modeling the force/EMG percentages. A mixed-effects negative-binomial model is proposed for modeling the fMRI counts. Then, I present a joint modeling approach to model the multidimensional outcomes together, which allows us to not only estimate the covariate effects but also to evaluate the strength of association among the multiple responses from different modalities. A simulation study is conducted to quantify the possible benefits by the new approaches in finite sample situations. Finally, the analysis of the fatigue data is illustrated with the use of the proposed methods.

Indexed as

Models, StatisticalNeurosciencesAnalysis of VarianceElectromyographyHand StrengthHumansMagnetic Resonance ImagingMuscle FatigueTreatment Outcome

Identifiers

PMID22522380
PMCPMC3353413

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.