Application of Longitudinal Item Response Theory Models to Modeling Parkinson's Disease Progression.
Abstract
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 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 3,573 early PD subjects and 14,904
visits, and mean follow-up time of 2.5 year (±1.57). We applied both a unidimensional
(each Part separately) and multidimensional (both Parts combined) longitudinal item
response theory (IRT) models. We assessed the progression rates for both parts, anchored
to baseline Hoehn & Yahr (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, while 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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https://hdl.handle.net/10161/25541Published Version (Please cite this version)
10.1002/psp4.12853Publication Info
Zou, Haotian; Aggarwal, Varun; Stebbins, Glenn T; Müller, Martijn LTM; Cedarbaum,
Jesse M; Pedata, Anne; ... Luo, Sheng (2022). Application of Longitudinal Item Response Theory Models to Modeling Parkinson's Disease
Progression. CPT: pharmacometrics & systems pharmacology. 10.1002/psp4.12853. Retrieved from https://hdl.handle.net/10161/25541.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Sheng Luo
Professor of Biostatistics & Bioinformatics
Haotian Zou
Postdoctoral Associate
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