Item Response Theory as an Efficient Tool to Describe a Heterogeneous Clinical Rating Scale in De Novo Idiopathic Parkinson’s Disease Patients
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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.
KEY WORDSdrug effect item response theory MDS-UPDRS Parkinson’s disease pharmacometrics
Coefficient of variation
End of trial
Item response theory
Likelihood ratio test
Movement Disorder Society UPDRS
Michael J. Fox Foundation
NON-linear mixed effect model
Parkinson’s Progression Markers Initiative
Relative standard error
Sum of item scores
Scans without Evidence of Dopaminergic Deficit
Unified Parkinson’s Disease Rating Scale
Visual predictive check
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