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Application of longitudinal item response theory models to modeling Parkinson’s disease progression

The Movement Disorder Society revised version of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS) parts 2 and 3 reflect patient‐reported functional impact and clinician‐reported severity of motor signs of Parkinson’s disease (PD), respectively. Total scores are common clinical outcomes but m...

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Autores principales: Zou, Haotian, Aggarwal, Varun, Stebbins, Glenn T., Müller, Martijn L. T. M., Cedarbaum, Jesse M., Pedata, Anne, Stephenson, Diane, Simuni, Tanya, Luo, Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9574723/
https://www.ncbi.nlm.nih.gov/pubmed/35895005
http://dx.doi.org/10.1002/psp4.12853
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author Zou, Haotian
Aggarwal, Varun
Stebbins, Glenn T.
Müller, Martijn L. T. M.
Cedarbaum, Jesse M.
Pedata, Anne
Stephenson, Diane
Simuni, Tanya
Luo, Sheng
author_facet Zou, Haotian
Aggarwal, Varun
Stebbins, Glenn T.
Müller, Martijn L. T. M.
Cedarbaum, Jesse M.
Pedata, Anne
Stephenson, Diane
Simuni, Tanya
Luo, Sheng
author_sort Zou, Haotian
collection PubMed
description The Movement Disorder Society revised version of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS) parts 2 and 3 reflect patient‐reported functional impact and clinician‐reported severity of motor signs of Parkinson’s disease (PD), respectively. Total scores are common clinical outcomes but may obscure important time‐based changes in items. We aim to analyze longitudinal disease progression based on MDS‐UPRDS parts 2 and 3 item‐level responses over time and as functions of Hoehn & Yahr (H&Y) stages 1 and 2 for subjects with early PD. The longitudinal item response theory (IRT) modeling is a novel statistical method addressing limitations in traditional linear regression approaches, such as ignoring varying item sensitivities and the sum score balancing out improvements and declines. We utilized a harmonized dataset consisting of six studies with 3573 subjects with early PD and 14,904 visits, and mean follow‐up time of 2.5 years (±1.57). We applied both a unidimensional (each part separately) and multidimensional (both parts combined) longitudinal IRT models. We assessed the progression rates for both parts, anchored to baseline H&Y stages 1 and 2. Both the uni‐ and multidimensional longitudinal IRT models indicate significant worsening time effects in both parts 2 and 3. Baseline H&Y stage 2 was associated with significantly higher baseline severities, but slower progression rates in both parts, as compared with stage 1. Patients with baseline H&Y stage 1 demonstrated slower progression in part 2 severity compared to part 3, whereas patients with baseline H&Y stage 2 progressed faster in part 2 than part 3. The multidimensional model had a superior fit compared to the unidimensional models and it had excellent model performance.
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spelling pubmed-95747232022-10-17 Application of longitudinal item response theory models to modeling Parkinson’s disease progression Zou, Haotian Aggarwal, Varun Stebbins, Glenn T. Müller, Martijn L. T. M. Cedarbaum, Jesse M. Pedata, Anne Stephenson, Diane Simuni, Tanya Luo, Sheng CPT Pharmacometrics Syst Pharmacol Research The Movement Disorder Society revised version of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS) parts 2 and 3 reflect patient‐reported functional impact and clinician‐reported severity of motor signs of Parkinson’s disease (PD), respectively. Total scores are common clinical outcomes but may obscure important time‐based changes in items. We aim to analyze longitudinal disease progression based on MDS‐UPRDS parts 2 and 3 item‐level responses over time and as functions of Hoehn & Yahr (H&Y) stages 1 and 2 for subjects with early PD. The longitudinal item response theory (IRT) modeling is a novel statistical method addressing limitations in traditional linear regression approaches, such as ignoring varying item sensitivities and the sum score balancing out improvements and declines. We utilized a harmonized dataset consisting of six studies with 3573 subjects with early PD and 14,904 visits, and mean follow‐up time of 2.5 years (±1.57). We applied both a unidimensional (each part separately) and multidimensional (both parts combined) longitudinal IRT models. We assessed the progression rates for both parts, anchored to baseline H&Y stages 1 and 2. Both the uni‐ and multidimensional longitudinal IRT models indicate significant worsening time effects in both parts 2 and 3. Baseline H&Y stage 2 was associated with significantly higher baseline severities, but slower progression rates in both parts, as compared with stage 1. Patients with baseline H&Y stage 1 demonstrated slower progression in part 2 severity compared to part 3, whereas patients with baseline H&Y stage 2 progressed faster in part 2 than part 3. The multidimensional model had a superior fit compared to the unidimensional models and it had excellent model performance. John Wiley and Sons Inc. 2022-08-09 2022-10 /pmc/articles/PMC9574723/ /pubmed/35895005 http://dx.doi.org/10.1002/psp4.12853 Text en © 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research
Zou, Haotian
Aggarwal, Varun
Stebbins, Glenn T.
Müller, Martijn L. T. M.
Cedarbaum, Jesse M.
Pedata, Anne
Stephenson, Diane
Simuni, Tanya
Luo, Sheng
Application of longitudinal item response theory models to modeling Parkinson’s disease progression
title Application of longitudinal item response theory models to modeling Parkinson’s disease progression
title_full Application of longitudinal item response theory models to modeling Parkinson’s disease progression
title_fullStr Application of longitudinal item response theory models to modeling Parkinson’s disease progression
title_full_unstemmed Application of longitudinal item response theory models to modeling Parkinson’s disease progression
title_short Application of longitudinal item response theory models to modeling Parkinson’s disease progression
title_sort application of longitudinal item response theory models to modeling parkinson’s disease progression
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9574723/
https://www.ncbi.nlm.nih.gov/pubmed/35895005
http://dx.doi.org/10.1002/psp4.12853
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