Pharmaceutical Research

, Volume 34, Issue 10, pp 2109–2118 | Cite as

Item Response Theory as an Efficient Tool to Describe a Heterogeneous Clinical Rating Scale in De Novo Idiopathic Parkinson’s Disease Patients

  • Simon Buatois
  • Sylvie Retout
  • Nicolas Frey
  • Sebastian Ueckert
Research Paper



This manuscript aims to precisely describe the natural disease progression of Parkinson’s disease (PD) patients and evaluate approaches to increase the drug effect detection power.


An item response theory (IRT) longitudinal model was built to describe the natural disease progression of 423 de novo PD patients followed during 48 months while taking into account the heterogeneous nature of the MDS-UPDRS. Clinical trial simulations were then used to compare drug effect detection power from IRT and sum of item scores based analysis under different analysis endpoints and drug effects.


The IRT longitudinal model accurately describes the evolution of patients with and without PD medications while estimating different progression rates for the subscales. When comparing analysis methods, the IRT-based one consistently provided the highest power.


IRT is a powerful tool which enables to capture the heterogeneous nature of the MDS-UPDRS.


drug effect item response theory MDS-UPDRS Parkinson’s disease pharmacometrics 



Coefficient of variation


End of trial


Inter-individual variability


Item response theory


Likelihood ratio test


Movement Disorder Society UPDRS


Michael J. Fox Foundation


NON-linear mixed effect model


Parkinson’s disease


Parkinson’s Progression Markers Initiative


Relative standard error


Standard deviation


Sum of item scores


Scans without Evidence of Dopaminergic Deficit


Unified Parkinson’s Disease Rating Scale


Visual predictive check

Supplementary material

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Supplementary Material A (DOCX 18 kb)
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Supplementary Material B (DOCX 18 kb)
11095_2017_2216_MOESM3_ESM.docx (5.9 mb)
Supplementary Material C (DOCX 6044 kb)
11095_2017_2216_MOESM4_ESM.docx (316 kb)
Supplementary Material D (DOCX 316 kb)


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Simon Buatois
    • 1
    • 2
    • 3
  • Sylvie Retout
    • 1
    • 3
  • Nicolas Frey
    • 1
  • Sebastian Ueckert
    • 4
  1. 1.Roche Pharma Research and Early Development, Clinical Pharmacology Roche Innovation Center BaselF. Hoffmann-La Roche LtdBaselSwitzerland
  2. 2.IAME, UMR 1137, INSERM, University Paris Diderot, Sorbonne Paris CitéParisFrance
  3. 3.INSTITUT ROCHE, Roche S.A.SBoulogne-BillancourtFrance
  4. 4.Department of Pharmaceutical BiosciencesUppsala UniversityUppsalaSweden

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