Novel Approach to Movement Disorder Society-Unified Parkinson's Disease Rating Scale Monitoring in Clinical Trials: Longitudinal Item Response Theory Models.
Bayesian modeling
Parkinson's disease
clinimetrics
disease progression
longitudinal data
Journal
Movement disorders clinical practice
ISSN: 2330-1619
Titre abrégé: Mov Disord Clin Pract
Pays: United States
ID NLM: 101630279
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
received:
04
06
2021
revised:
05
07
2021
accepted:
09
07
2021
entrez:
11
10
2021
pubmed:
12
10
2021
medline:
12
10
2021
Statut:
epublish
Résumé
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. 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. 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. 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). 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.
Sections du résumé
BACKGROUND
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
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
METHODS
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
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
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.
Identifiants
pubmed: 34631944
doi: 10.1002/mdc3.13311
pii: MDC313311
pmc: PMC8485609
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1083-1091Subventions
Organisme : NIA NIH HHS
ID : R01 AG064803
Pays : United States
Informations de copyright
© 2021 The Authors. Movement Disorders Clinical Practice published by Wiley Periodicals LLC. on behalf of International Parkinson and Movement Disorder Society.
Déclaration de conflit d'intérêts
The research of Sheng Luo was supported by National Institute on Aging (Grant R01AG064803). The Rush Parkinson's Disease and Movement Disorders Program is a designated Clinical Center of Excellent supported by the Parkinson Foundation. The authors have no conflicts of interest to report.
Références
Ann Intern Med. 2020 May 5;172(9):591-598
pubmed: 32227247
Pharm Res. 2017 Oct;34(10):2109-2118
pubmed: 28695401
AAPS J. 2017 May;19(3):837-845
pubmed: 28247193
AAPS J. 2017 Jan;19(1):172-179
pubmed: 27634384
Br J Clin Pharmacol. 2021 Sep;87(9):3608-3618
pubmed: 33580584
Mov Disord Clin Pract. 2018 Jan-Feb;5(1):47-53
pubmed: 29662921
CPT Pharmacometrics Syst Pharmacol. 2017 Sep;6(9):635-641
pubmed: 28643388
Ann Appl Stat. 2017 Sep;11(3):1787-1809
pubmed: 29081873
Mov Disord. 2020 Sep;35(9):1587-1595
pubmed: 32469456
Pharm Res. 2019 Jul 17;36(9):135
pubmed: 31317279
J Pharmacokinet Pharmacodyn. 2020 Oct;47(5):461-471
pubmed: 32617833
Pharm Res. 2014 Aug;31(8):2152-65
pubmed: 24595495
J Neurol. 2019 Aug;266(8):1927-1936
pubmed: 31073716
CPT Pharmacometrics Syst Pharmacol. 2017 Aug;6(8):543-551
pubmed: 28571119
Stat Med. 2017 Sep 10;36(20):3244-3256
pubmed: 28569393