Evidence map›Paper›PMID 34631944›Full record

ArticleMovement disorders clinical practice2021

Novel Approach to Movement Disorder Society-Unified Parkinson's Disease Rating Scale Monitoring in Clinical Trials: Longitudinal Item Response Theory Models.

Sheng Luo, Haotian Zou, Christopher G Goetz, Dongrak Choi, David Oakes, Tanya Simuni, Glenn T Stebbins

Open access · bronzeAbstract read
In one paragraph

Article in Movement disorders clinical practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

9 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling.Movement disorders : official journal of the Movement Disorder Society · 2022
    Article
  9. 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

7 authors at 5 institutions in 2 countries.

Sheng LuoDepartment of Biostatistics and Bioinformatics Duke University Durham North Carolina USA.ORCID https://orcid.org/0000-0003-4214-5809
Haotian ZouDepartment of Biostatistics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA.
Christopher G GoetzDepartment of Neurological Sciences, Section of Movement Disorders Rush University Medical Center Chicago Illinois USA.
Dongrak ChoiDepartment of Biostatistics and Bioinformatics Duke University Durham North Carolina USA.
David OakesUniversity of Rochester Medical Center Department of Biostatistics and Computational Biology Rochester New York USA.
Tanya SimuniParkinson's disease and Movement Disorders Center Northwestern University Medical Center Chicago Illinois USA.
Glenn T StebbinsDepartment of Neurological Sciences, Section of Movement Disorders Rush University Medical Center Chicago Illinois USA.
Duke University · USRush University Medical Center · USNorthwestern University · USUniversity of North Carolina at Chapel Hill · USUniversity of Rochester Medical Center · US

Funding

Integrative modeling and dynamic prediction of Alzheimer's diseaseR01AG064803 · NIA · DUKE UNIVERSITY · PI LUO, SHENG · 2020 to 2024
$2.3M
Statistical Methods for Clinical Trials with Multivariate Longitudinal OutcomesR01NS091307 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI LUO, SHENG · 2015 to 2018
$1.2M
NIA NIH HHS R01 AG064803NINDS NIH HHS R01 NS091307
6 · The paper itself

Abstract

backgroundAlthough nontremor and tremor Part 3 Movement Disorder Society-Unified Parkinson's Disease Rating Scale items measure different impairment domains, their distinct progression and drug responsivity remain unstudied longitudinally. The total score may obscure important time-based and treatment-based changes occurring in the individual domains.

objectiveUsing the unique advantages of item response theory (IRT), we developed novel longitudinal unidimensional and multidimensional models to investigate nontremor and tremor changes occurring in an interventional Parkinson's disease (PD) study.

methodWith unidimensional longitudinal IRT, we assessed the 33 Part 3 item data (22 nontremor and 10 tremor items) of 336 patients with early PD from the STEADY-PD III (Safety, Tolerability, and Efficacy Assessment of Isradipine for PD, placebo vs. isradipine) study. With multidimensional longitudinal IRT, we assessed the progression rates over time and treatment (in overall motor severity, nontremor, and tremor domains) using Markov Chain Monte Carlo implemented in Stan.

resultsRegardless of treatment, patients showed significant but different time-based deterioration rates for total motor, nontremor, and tremor scores. Isradipine was associated with additional significant deterioration over placebo in total score and nontremor scores, but not in tremor score. Further highlighting the 2 separate latent domains, nontremor and tremor severity changes were positively but weakly correlated (correlation coefficient, 0.108).

conclusionsLongitudinal IRT analysis is a novel statistical method highly applicable to PD clinical trials. It addresses limitations of traditional linear regression approaches and previous IRT investigations that either applied cross-sectional IRT models to longitudinal data or failed to estimate all parameters simultaneously. It is particularly useful because it can separate nontremor and tremor changes both over time and in response to treatment interventions.

Indexed as

Bayesian modelingclinimetricsdisease progressionlongitudinal dataParkinson's disease

Identifiers

PMID34631944
PMCPMC8485609
OpenAlexW3185944424

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.