An Innovative Pharmacometric Approach for the Simultaneous Analysis of Frequency, Duration and Severity of Migraine Events.

PURPOSE: To explore the use of a multistate repeated, time-to-categorical event model describing the frequency, severity and duration of migraines. METHODS: Subject level data from patients in placebo arms from two efficacy trials for migraine-preventive treatments were used. Models were developed using NONMEM 7.3. A survival model was combined with an ordered categorical model to form the repeated-time-to-start of categorical migraine event model, which simultaneously described the time-to-start of migraines and the severity of the starting migraine event. This was linked to a repeated-time-to-end of migraine event model with different hazard functions depending on the severity of the ongoing migraine event. Model performance was internally and externally qualified. RESULTS: The successfully qualified model showed that patients responding to placebo had a reduction in migraine incidence rate, and a decreased proportion of severe migraines. There was an increase in moderate migraine duration, an increased proportion of mild migraines and a reduction in proportion of severe migraines. Age was related to migraine duration. CONCLUSIONS: The model represents an innovative framework for clinical trial modeling and simulation, and successfully describes placebo effect in migraine prevention. This approach can be adapted to investigate exposure-response relationship of drugs and can also be implemented in other therapeutic areas where the rate, duration and severity of disease episodes are relevant to trial outcomes.

Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson's Disease Rating Scales.

PURPOSE: The aim of this work was to allow combination of information from recent and historical trials in Parkinson's Disease (PD) by developing bridging methodology between two versions of the clinical endpoint. METHODS: A previously developed Item Response Model (IRM), that described longitudinal changes in Movement Disorder Society (MDS) sponsored revision of Unified Parkinson's Disease Rating Scale (UPDRS) [MDS-UPDRS] data from the De Novo PD cohort in Parkinson's Progression Markers Initiative, was first adapted to describe baseline UPDRS data from two clinical trials, one in subjects with early PD and another in subjects with advanced PD. Assuming similar IRM structure, items of the UPDRS version were mapped to those in the MDS-UPDRS version. Subsequently, the longitudinal changes in the placebo arm of the advanced PD study were characterized. RESULTS: The parameters reflecting differences in the shared items between endpoints were successfully estimated, and the model diagnostics indicated that mapping was better for early PD subjects (closer to De Novo cohort) than for advanced PD subjects. Disease progression for placebo in advanced PD patients was relatively shallow. CONCLUSION: An IRM able to handle two variants of clinical PD endpoints was developed; it can improve the utilization of data from diverse sources and diverse disease populations.

Modeling a Composite Score in Parkinson's Disease Using Item Response Theory.

In the current work, we present the methodology for development of an Item Response Theory model within a non-linear mixed effects framework to characterize the longitudinal changes of the Movement Disorder Society (sponsored revision) of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) endpoint in Parkinson's disease (PD). The data were obtained from Parkinson's Progression Markers Initiative database and included 163,070 observations up to 48 months from 430 subjects belonging to De Novo PD cohort. The probability of obtaining a score, reported for each of the items in the questionnaire, was modeled as a function of the subject's disability. Initially, a single latent variable model was explored to characterize the disease progression over time. However, based on the understanding of the questionnaire set-up and the results of a residuals-based diagnostic tool, a three latent variable model with a mixture implementation was able to adequately describe longitudinal changes not only at the total score level but also at each individual item level. The linear progression rates obtained for the patient-reported items and the non-sided items were similar, each of which roughly take about 50 months for a typical subject to progress linearly from the baseline by one standard deviation. However for the sided items, it was found that the better side deteriorates quicker than the disabled side. This study presents a framework for analyzing MDS-UPDRS data, which can be adapted to more traditional UPDRS data collected in PD clinical trials and result in more efficient designs and analyses of such studies.