Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling.
Bayesian modeling
Parkinson's disease
disease progression
Journal
Movement disorders : official journal of the Movement Disorder Society
ISSN: 1531-8257
Titre abrégé: Mov Disord
Pays: United States
ID NLM: 8610688
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
revised:
23
05
2022
received:
13
01
2022
accepted:
17
06
2022
pubmed:
17
7
2022
medline:
16
9
2022
entrez:
16
7
2022
Statut:
ppublish
Résumé
Longitudinal 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. Apply 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. We 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. The 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. Longitudinal 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.
Sections du résumé
BACKGROUND
Longitudinal 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.
OBJECTIVE
Apply 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.
METHODS
We 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.
RESULTS
The 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.
CONCLUSIONS
Longitudinal 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.
Identifiants
pubmed: 35841312
doi: 10.1002/mds.29154
pmc: PMC9897939
mid: NIHMS1819131
doi:
Substances chimiques
Uric Acid
268B43MJ25
Levodopa
46627O600J
Inosine
5A614L51CT
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1904-1914Subventions
Organisme : NIA NIH HHS
ID : P30 AG028716
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG072958
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG064803
Pays : United States
Informations de copyright
© 2022 International Parkinson and Movement Disorder Society.
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