Novel Approach to Movement Disorder Society–Unified Parkinson's Disease Rating Scale Monitoring in Clinical Trials: Longitudinal Item Response Theory Models

Sheng Luo, Haotian Zou, Christopher G. Goetz, Dongrak Choi, David Oakes, Tanya Simuni, Glenn T. Stebbins*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Although nontremor and tremor Part 3 Movement Disorder Society–Unified Parkinson's Disease Rating Scale items measure different impairment domains, their distinct progression and drug responsivity remain unstudied longitudinally. The total score may obscure important time-based and treatment-based changes occurring in the individual domains. Objective: Using the unique advantages of item response theory (IRT), we developed novel longitudinal unidimensional and multidimensional models to investigate nontremor and tremor changes occurring in an interventional Parkinson's disease (PD) study. Method: With unidimensional longitudinal IRT, we assessed the 33 Part 3 item data (22 nontremor and 10 tremor items) of 336 patients with early PD from the STEADY-PD III (Safety, Tolerability, and Efficacy Assessment of Isradipine for PD, placebo vs. isradipine) study. With multidimensional longitudinal IRT, we assessed the progression rates over time and treatment (in overall motor severity, nontremor, and tremor domains) using Markov Chain Monte Carlo implemented in Stan. Results: Regardless of treatment, patients showed significant but different time-based deterioration rates for total motor, nontremor, and tremor scores. Isradipine was associated with additional significant deterioration over placebo in total score and nontremor scores, but not in tremor score. Further highlighting the 2 separate latent domains, nontremor and tremor severity changes were positively but weakly correlated (correlation coefficient, 0.108). Conclusions: Longitudinal IRT analysis is a novel statistical method highly applicable to PD clinical trials. It addresses limitations of traditional linear regression approaches and previous IRT investigations that either applied cross-sectional IRT models to longitudinal data or failed to estimate all parameters simultaneously. It is particularly useful because it can separate nontremor and tremor changes both over time and in response to treatment interventions.

Original languageEnglish (US)
Pages (from-to)1083-1091
Number of pages9
JournalMovement Disorders Clinical Practice
Volume8
Issue number7
DOIs
StatePublished - Oct 2021

Keywords

  • Bayesian modeling
  • Parkinson's disease
  • clinimetrics
  • disease progression
  • longitudinal data

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology

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