One of the most frequently used assessments in clinical trials of Parkinson’s Disease is the Movement Disorders Society version of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Producing high-quality data is crucial for demonstrating efficacy for new products in development. A recent study published in Movement Disorders examined frequency of MDS-UPDRS rater errors in the Parkinson’s Progression Markers Initiative (PPMI) cohort.
The study looked at the motor examination in Part III performed by site investigators who had completed online training for the MDS-UPDRS. In PPMI, investigators record ratings on a paper case report form and then enter the data into an electronic data capture (EDC) system. Inconsistencies were flagged based on the following criteria:
- Resting tremor amplitude without constancy (or the reverse)
- Hoehn and Yahr Stage 2 ratings with unilateral symptoms
- Opposite lateralization of resting tremor or rigidity between upper and lower extremities
Based on these criteria, inconsistencies were identified for 11.8% of participants at baseline or first follow-up. The study recommends direct data capture, such as an electronic clinical outcome assessments (eCOA) platform as a mechanism to reduce inconsistencies and errors. One way eCOA can reduce errors in this situation is by eliminating transcription errors that occur when one value is written on the case report form and then incorrectly entered into the EDC system.
More importantly, eCOA allows for real-time data checking that can flag likely errors, prompting raters to address entries that appear inconsistent or fall outside expectations. For example, if severe postural instability was recorded without any gait disturbance, a flag would be raised to reconcile or reconfirm this unusual combination. Systems can also be configured to check for unlikely changes over time, such as reverse lateralization. These real-time checks are important because investigators can confirm the values during the visit rather than relying on memory if an anomaly is found later.
At VeraSci, we believe that well-designed, science-driven processes and technologies are the best cure for sluggish timelines and noisy data.