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.