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.