INTRODUCTION
Parkinson’s disease (PD) is a chronic and progressive neurodegenerative
condition with about 6.2 million patients worldwide1.
Motor neuron deterioration in the brain is the key characteristic of the
disease2. Unfortunately, no definitive biomarker for
PD has been identified3. A Unified Parkinson’s Disease
Rating Scale (UPDRS) was originally developed as a clinical measure for
symptom severity among markedly and severely disabled
patients4. Later, a Movement Disorder Society version
of UPDRS (MDS-UPDRS) was introduced for early diagnosis to measure
milder deficit and smaller changes in the early disease stage, focusing
on broader and lower ranges in disability than the original UPDRS. The
MDS-UPDRS consists of four parts, reflecting different aspects of the
clinical manifestation of the disease5,6. The outcome
of the assessment is a sum of scores (SoS) of multiple items in each
part, and a total score (TS) for all parts. Using these composite scores
for evaluating disease severity and treatment effects requires large
sample sizes to avoid inconclusive drug trials, especially for disease
modifying treatments7. An alternative analytical
approach that can enhance the signal-to-noise ratio would open the path
for more efficient and rigorous clinical trials of PD therapies.
Item Response Theory (IRT) modelling describes the relationships between
the trait of interest and the items that are used to measure the trait;
therefore, it is a promising approach for analyzing itemized
scales8. Instead of relying on a single composite
score of the test, it defines mathematical links for individual items in
the instrument to directly estimate a patient’s disease severity that
the very instrument is designed to measure. For its improved utilization
of the data at the item level, IRT has been applied in the research of
several neurological diseases such as Parkinson’s
disease12,22,28, Alzheimer’s
disease9, multiple sclerosis10 and
schizophrenia11. Remarkably, the methodology have
shown promise to significantly reduce the size of drug trials9,22.
Demonstrating the ability to delay motor impairment is essential for a
drug aimed to slow down PD progression. Longitudinal IRT models has been
developed using MDS-UPDRS to describe the progression of
PD12,22. The models included the assessments of
non-motor domains, as well as interaction terms among items of different
domains. The goal of the current analysis was to assess the IRT’s
ability to enhance the efficiency for detecting drug effect on MDS-UPDRS
Part III – motor examinations – which is considered as a more
objective endpoint of motor function, hence central to diagnostic and
therapeutic assessments. Specifically, the aims were to: i) develop an
IRT model for estimating symptom severity using item scores of MDS-UPDRS
Part III, ii) use the IRT model to explore relative importance of the
items, iii) build longitudinal models to describe symptom progression
over time in terms of SoS and symptom severity, and iv) compare the
probability of trial success when analyzed using symptom severity or SoS
for a potential disease-modifying new treatment with uncertain effect.