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

ABSTRACT 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 dif...

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Published in:Movement disorders 2022-09, Vol.37 (9), p.1904-1914
Main Authors: Luo, Sheng, Zou, Haotian, Stebbins, Glenn T., Schwarzschild, Michael A., Macklin, Eric A., Chan, James, Oakes, David, Simuni, Tanya, Goetz, Christopher G.
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cited_by cdi_FETCH-LOGICAL-c3884-99ecf0672b4bda8865425fdcd81cecd5165047c3849de3e4fa1b9a8cebbf60003
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container_end_page 1914
container_issue 9
container_start_page 1904
container_title Movement disorders
container_volume 37
creator Luo, Sheng
Zou, Haotian
Stebbins, Glenn T.
Schwarzschild, Michael A.
Macklin, Eric A.
Chan, James
Oakes, David
Simuni, Tanya
Goetz, Christopher G.
description ABSTRACT 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.
doi_str_mv 10.1002/mds.29154
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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.</description><identifier>ISSN: 0885-3185</identifier><identifier>ISSN: 1531-8257</identifier><identifier>EISSN: 1531-8257</identifier><identifier>DOI: 10.1002/mds.29154</identifier><identifier>PMID: 35841312</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley &amp; Sons, Inc</publisher><subject>Bayes Theorem ; Bayesian analysis ; Bayesian modeling ; Clinical trials ; disease progression ; Humans ; Inosine ; Item response theory ; Levodopa ; Mathematical models ; Movement disorders ; Neurodegenerative diseases ; Neurological complications ; Neurological diseases ; Parkinson Disease - complications ; Parkinson's disease ; Placebos ; Severity of Illness Index ; Statistics ; Tremor ; Tremor (Muscular contraction) ; Tremor - diagnosis ; Uric Acid</subject><ispartof>Movement disorders, 2022-09, Vol.37 (9), p.1904-1914</ispartof><rights>2022 International Parkinson and Movement Disorder Society.</rights><rights>2022 International Parkinson and Movement Disorder Society</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3884-99ecf0672b4bda8865425fdcd81cecd5165047c3849de3e4fa1b9a8cebbf60003</citedby><cites>FETCH-LOGICAL-c3884-99ecf0672b4bda8865425fdcd81cecd5165047c3849de3e4fa1b9a8cebbf60003</cites><orcidid>0000-0003-4214-5809</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35841312$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Luo, Sheng</creatorcontrib><creatorcontrib>Zou, Haotian</creatorcontrib><creatorcontrib>Stebbins, Glenn T.</creatorcontrib><creatorcontrib>Schwarzschild, Michael A.</creatorcontrib><creatorcontrib>Macklin, Eric A.</creatorcontrib><creatorcontrib>Chan, James</creatorcontrib><creatorcontrib>Oakes, David</creatorcontrib><creatorcontrib>Simuni, Tanya</creatorcontrib><creatorcontrib>Goetz, Christopher G.</creatorcontrib><creatorcontrib>Parkinson Study Group SURE-PD3 Investigators</creatorcontrib><creatorcontrib>Parkinson Study Group SURE‐PD3 Investigators</creatorcontrib><title>Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling</title><title>Movement disorders</title><addtitle>Mov Disord</addtitle><description>ABSTRACT 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.</description><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bayesian modeling</subject><subject>Clinical trials</subject><subject>disease progression</subject><subject>Humans</subject><subject>Inosine</subject><subject>Item response theory</subject><subject>Levodopa</subject><subject>Mathematical models</subject><subject>Movement disorders</subject><subject>Neurodegenerative diseases</subject><subject>Neurological complications</subject><subject>Neurological diseases</subject><subject>Parkinson Disease - complications</subject><subject>Parkinson's disease</subject><subject>Placebos</subject><subject>Severity of Illness Index</subject><subject>Statistics</subject><subject>Tremor</subject><subject>Tremor (Muscular contraction)</subject><subject>Tremor - diagnosis</subject><subject>Uric Acid</subject><issn>0885-3185</issn><issn>1531-8257</issn><issn>1531-8257</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp10EFv0zAYxnELgVgpHPgCyBKHwSGdHceJzW1aYVTqNATjHDn2m9YjiYvfRKjffoZsHCbtZB9--st-CHnL2Yozlp_1Dle55rJ4RhZcCp6pXFbPyYIpJTPBlTwhrxBvGeNc8vIlORFSFVzwfEF2a48IdvTDjo57oOvQGz8gDS39ZuKvdA3DKdKkwCB8opsB_W4_Im1j6Ok2DDs_Ts4PpqObEXr6HfAQBgR6s4cQj_QqOOhS_DV50ZoO4c39uSQ_v3y-ufiaba8vNxfn28wKpYpMa7AtK6u8KRpnlCplkcvWWae4BevS6yUrqmQL7UBA0RreaKMsNE1bMsbEknyYu4cYfk-AY917tNB1ZoAwYZ2XmjMpcl0l-v4RvQ1TTD9JquJCC53mS-rjrGwMiBHa-hB9b-Kx5qz-u36d1q__rZ_su_vi1PTg_suHuRM4m8Ef38Hx6VJ9tf4xJ-8Ad0aOuQ</recordid><startdate>202209</startdate><enddate>202209</enddate><creator>Luo, Sheng</creator><creator>Zou, Haotian</creator><creator>Stebbins, Glenn T.</creator><creator>Schwarzschild, Michael A.</creator><creator>Macklin, Eric A.</creator><creator>Chan, James</creator><creator>Oakes, David</creator><creator>Simuni, Tanya</creator><creator>Goetz, Christopher G.</creator><general>John Wiley &amp; 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Medical Complete (Alumni)</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Movement disorders</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luo, Sheng</au><au>Zou, Haotian</au><au>Stebbins, Glenn T.</au><au>Schwarzschild, Michael A.</au><au>Macklin, Eric A.</au><au>Chan, James</au><au>Oakes, David</au><au>Simuni, Tanya</au><au>Goetz, Christopher G.</au><aucorp>Parkinson Study Group SURE-PD3 Investigators</aucorp><aucorp>Parkinson Study Group SURE‐PD3 Investigators</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling</atitle><jtitle>Movement disorders</jtitle><addtitle>Mov Disord</addtitle><date>2022-09</date><risdate>2022</risdate><volume>37</volume><issue>9</issue><spage>1904</spage><epage>1914</epage><pages>1904-1914</pages><issn>0885-3185</issn><issn>1531-8257</issn><eissn>1531-8257</eissn><abstract>ABSTRACT 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.</abstract><cop>Hoboken, USA</cop><pub>John Wiley &amp; Sons, Inc</pub><pmid>35841312</pmid><doi>10.1002/mds.29154</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-4214-5809</orcidid><oa>free_for_read</oa></addata></record>
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source Wiley-Blackwell Read & Publish Collection
subjects Bayes Theorem
Bayesian analysis
Bayesian modeling
Clinical trials
disease progression
Humans
Inosine
Item response theory
Levodopa
Mathematical models
Movement disorders
Neurodegenerative diseases
Neurological complications
Neurological diseases
Parkinson Disease - complications
Parkinson's disease
Placebos
Severity of Illness Index
Statistics
Tremor
Tremor (Muscular contraction)
Tremor - diagnosis
Uric Acid
title Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling
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