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
  • 316 Downloads

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

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

KEY WORDS

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

ABBREVIATIONS

CV

Coefficient of variation

EOT

End of trial

IIV

Inter-individual variability

IRT

Item response theory

LRT

Likelihood ratio test

MDS-UPDRS

Movement Disorder Society UPDRS

MJFF

Michael J. Fox Foundation

NONMEM

NON-linear mixed effect model

PD

Parkinson’s disease

PPMI

Parkinson’s Progression Markers Initiative

RSE

Relative standard error

SD

Standard deviation

SIS

Sum of item scores

SWEDD

Scans without Evidence of Dopaminergic Deficit

UPDRS

Unified Parkinson’s Disease Rating Scale

VPC

Visual predictive check

Supplementary material

11095_2017_2216_MOESM1_ESM.docx (18 kb)
Supplementary Material A (DOCX 18 kb)
11095_2017_2216_MOESM2_ESM.docx (18 kb)
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)

References

  1. 1.
    Pringsheim T, Jette N, Frolkis A, Steeves TDL. The prevalence of Parkinson’s disease: a systematic review and meta-analysis. Mov Disord Off J Mov Disord Soc. 2014;29(13):1583–90.CrossRefGoogle Scholar
  2. 2.
    Samii A, Nutt JG, Ransom BR. Parkinson’s disease. Lancet Lond Engl. 2004;363(9423):1783–93.CrossRefGoogle Scholar
  3. 3.
    Hughes AJ, Daniel SE, Blankson S, Lees AJ. A clinicopathologic study of 100 cases of Parkinson’s disease. Arch Neurol. 1993;50(2):140–8.CrossRefPubMedGoogle Scholar
  4. 4.
    Braak H, Ghebremedhin E, Rüb U, Bratzke H, Del Tredici K. Stages in the development of Parkinson’s disease-related pathology. Cell Tissue Res. 2004;318(1):121–34.CrossRefPubMedGoogle Scholar
  5. 5.
    Frasier M, Kang UJ. Parkinson’s Disease Biomarkers: Resources for Discovery and Validation. Neuropsychopharmacology. 2014;39(1):241–2.CrossRefPubMedGoogle Scholar
  6. 6.
    Kalia LV, Lang AE. Parkinson’s disease. Lancet Lond Engl. 2015;386(9996):896–912.CrossRefGoogle Scholar
  7. 7.
    Ramaker C, Marinus J, Stiggelbout AM, Van Hilten BJ. Systematic evaluation of rating scales for impairment and disability in Parkinson’s disease. Mov Disord Off J Mov Disord Soc. 2002;17(5):867–76.CrossRefGoogle Scholar
  8. 8.
    Holford NHG, Chan PLS, Nutt JG, Kieburtz K, Shoulson I, Parkinson Study Group. Disease progression and pharmacodynamics in Parkinson disease - evidence for functional protection with levodopa and other treatments. J Pharmacokinet Pharmacodyn. 2006;33(3):281–311.CrossRefPubMedGoogle Scholar
  9. 9.
    Vu TC, Nutt JG, Holford NHG. Progression of motor and nonmotor features of Parkinson’s disease and their response to treatment. Br J Clin Pharmacol. 2012;74(2):267–83.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Perlmutter JS. Assessment of Parkinson Disease Manifestations. Curr Protoc Neurosci Editor Board Jacqueline N Crawley Al. 2009;CHAPTER:Unit10.1.Google Scholar
  11. 11.
    Movement Disorder Society Task Force on Rating Scales for Parkinson’s Disease. The Unified Parkinson’s Disease Rating Scale (UPDRS): status and recommendations. Mov Disord Off J Mov Disord Soc. 2003;18(7):738–50.CrossRefGoogle Scholar
  12. 12.
    Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, et al. Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord Off J Mov Disord Soc. 2008;23(15):2129–70.CrossRefGoogle Scholar
  13. 13.
    Louis ED, Tang MX, Cote L, Alfaro B, Mejia H, Marder K. Progression of parkinsonian signs in Parkinson disease. Arch Neurol. 1999;56(3):334–7.CrossRefPubMedGoogle Scholar
  14. 14.
    Zetusky WJ, Jankovic J, Pirozzolo FJ. The heterogeneity of Parkinson’s disease: clinical and prognostic implications. Neurology. 1985;35(4):522–6.CrossRefPubMedGoogle Scholar
  15. 15.
    Chaudhuri KR, Schapira AHV. Non-motor symptoms of Parkinson’s disease: dopaminergic pathophysiology and treatment. Lancet Neurol. 2009;8(5):464–74.CrossRefPubMedGoogle Scholar
  16. 16.
    Poewe W, Hauser RA, Lang A, ADAGIO Investigators. Effects of rasagiline on the progression of nonmotor scores of the MDS-UPDRS. Mov Disord Off J Mov Disord Soc. 2015;30(4):589–92.CrossRefGoogle Scholar
  17. 17.
    Jankovic J, Kapadia AS. Functional decline in Parkinson disease. Arch Neurol. 2001;58(10):1611–5.CrossRefPubMedGoogle Scholar
  18. 18.
    Baker FB. The basics of item response theory. 2nd ed. College Park: ERIC Clearinghouse on Assessment and Evaluation; 2001.Google Scholar
  19. 19.
    Ueckert S, Plan EL, Ito K, Karlsson MO, Corrigan B, Hooker AC. Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling. Pharm Res. 2014;31(8):2152–65.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Gottipati G, Karlsson MO, Plan EL. Modeling a Composite Score in Parkinson’s Disease Using Item Response Theory. AAPS J. 2017;Google Scholar
  21. 21.
    Hoehn MM, Yahr MD. Parkinsonism: onset, progression and mortality. Neurology. 1967;17(5):427–42.CrossRefPubMedGoogle Scholar
  22. 22.
    Kjellsson MC, Zingmark P-H, Jonsson EN, Karlsson MO. Comparison of proportional and differential odds models for mixed-effects analysis of categorical data. J Pharmacokinet Pharmacodyn. 2008;35(5):483–501.CrossRefPubMedGoogle Scholar
  23. 23.
    Beal S, Sheiner LB, Boeckmann A, Bauer RJ. NONMEM user’s guides. (1989–2009). Icon Development Solutions, Ellicott City, MD USA; 2009.Google Scholar
  24. 24.
    Vu TC, Nutt JG, Holford NHG. Disease progress and response to treatment as predictors of survival, disability, cognitive impairment and depression in Parkinson’s disease. Br J Clin Pharmacol. 2012;74(2):284–95.CrossRefPubMedPubMedCentralGoogle Scholar

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

Personalised recommendations