Evidence map›Paper›PMID 35841312›Full record

ArticleMovement disorders : official journal of the Movement Disorder Society2022

Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling.

Sheng Luo, Haotian Zou, Glenn T Stebbins, Michael A Schwarzschild, Eric A Macklin, James Chan, David Oakes, Tanya Simuni, Christopher G Goetz, Parkinson Study Group SURE-PD3 Investigators

Open access · greenAbstract read
In one paragraph

Article in Movement disorders : official journal of the Movement Disorder Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.9field-weighted citation impact, top 13% 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

13 citing papers in PubMed, 16 citations in OpenAlex.

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  7. Tips and tricks in tremor treatment.Journal of neural transmission (Vienna, Austria : 1996) · 2024
    Review
  8. Article
  9. Article
  10. Role of dopamine agonists in Parkinson's disease therapy.Journal of neural transmission (Vienna, Austria : 1996) · 2023
    Review
  11. Article
  12. Article
  13. 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

10 authors at 7 institutions in 1 country.

Sheng LuoDepartment of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina, USA.ORCID 0000-0003-4214-5809
Haotian ZouDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Glenn T StebbinsDepartment of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA.
Michael A SchwarzschildDepartment of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Eric A MacklinBiostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
James ChanBiostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
David OakesDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.
Tanya SimuniDepartment of Neurology, Northwestern University Medical Center, Chicago, Illinois, USA.
Christopher G GoetzDepartment of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA.
Parkinson Study Group SURE-PD3 Investigators
Harvard University · USRush University Medical Center · USDuke University · USMassachusetts General Hospital · USNorthwestern University · USUniversity of North Carolina at Chapel Hill · USUniversity of Rochester Medical Center · US

Funding

Resource Core 3 - Metabolomics CoreP30AG028716 · NIA · DUKE UNIVERSITY · PI Virginia B Kraus · 2006 to 2026
$24.6M
Research Education Component CoreP30AG072958 · NIA · DUKE UNIVERSITY · PI Maria Marquine · 2021 to 2026
$24.1M
Phase 3 trial of inosine for Parkinson's disease CCCU01NS090259 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI SCHWARZSCHILD, MICHAEL A · 2015 to 2019
$16.9M
Phase 3 trial of inosine for Parkinson's disease - DCCU01NS089666 · NINDS · UNIVERSITY OF ROCHESTER · PI OAKES, DAVID · 2015 to 2019
$4.9M
Integrative modeling and dynamic prediction of Alzheimer's diseaseR01AG064803 · NIA · DUKE UNIVERSITY · PI LUO, SHENG · 2020 to 2024
$2.3M
NIA NIH HHS P30 AG028716NIA NIH HHS P30 AG072958NIA NIH HHS R01 AG064803NINDS NIH HHS U01 NS089666NINDS NIH HHS U01 NS090259
6 · The paper itself

Abstract

backgroundLongitudinal item response theory (IRT) models previously suggested that the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor examination has two salient domains, tremor and nontremor, that progress in time and in response to treatment differently.

objectiveApply longitudinal IRT modeling, separating tremor and nontremor domains, to reanalyze outcomes in the previously published clinical trial (Study of Urate Elevation in Parkinson's Disease, Phase 3) that showed no overall treatment effects.

methodsWe applied unidimensional and multidimensional longitudinal IRT models to MDS-UPDRS motor examination items in 298 participants with Parkinson's disease from the Study of Urate Elevation in Parkinson's Disease, Phase 3 (placebo vs. inosine) study. We separated 10 tremor items from 23 nontremor items and used Bayesian inference to estimate progression rates and sensitivity to treatment in overall motor severity and tremor and nontremor domains.

resultsThe progression rate was faster in the tremor domain than the nontremor domain before levodopa treatment. Inosine treatment had no effect on either domain relative to placebo. Levodopa treatment was associated with greater slowing of progression in the tremor domain than the nontremor domain regardless of inosine exposure. Linear patterns of progression were observed. Despite different domain-specific progression patterns, tremor and nontremor severities at baseline and over time were significantly correlated.

conclusionsLongitudinal IRT analysis is a novel statistical method addressing limitations of traditional linear regression approaches. It is particularly useful because it can simultaneously monitor changes in different, but related, domains over time and in response to treatment interventions. We suggest that in neurological diseases with distinct impairment domains, clinical or anatomical, this application may identify patterns of change unappreciated by standard statistical methods. © 2022 International Parkinson and Movement Disorder Society.

Indexed as

Parkinson DiseaseBayes TheoremHumansInosineLevodopaSeverity of Illness IndexTremorUric AcidInosineLevodopaUric AcidBayesian modelingdisease progressionParkinson's disease

Identifiers

PMID35841312
PMCPMC9897939
OpenAlexW4285588025

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.