Harmonized Data Quality Indicators Maintain Data Quality in Long-Term Safety Studies Using Multiple Sclerosis Registries/Data Sources: Experience from the CLARION Study

Abstract

<p>PURPOSE: Understanding the long-term safety of disease-modifying therapies for multiple sclerosis (MS) in routine clinical practice can be undertaken through registry-based studies. However, variability of data quality across such sources poses the challenge of data fit for regulatory decision-making. CLARION, a non-interventional cohort safety study of cladribine tablets, combines aggregated data from MS registries/data sources, except in Germany (which utilizes primary data collection). We describe the application of key data quality indicators (DQIs) within CLARION to evaluate data quality over time, as recommended by the European Medicines Agency (EMA) guideline on registry-based studies.<br></p><p>METHODS: DQIs were defined with participating registries/sources; they were used to assess data quality according to the EMA Data Quality Framework, addressing consistency, accuracy, completeness, and study representativeness. DQIs were associated with potential remedial measures if data quality was not met. DQIs were summarized overall and for individual MS registries/data sources to November 1, 2022.<br></p><p>RESULTS: A total of 28 DQIs were analyzed using data from 5069 patients arising from eight MS registries/data sources and 14 countries. The Representativeness DQIs showed that 72.0% of patients were female, median age at MS diagnosis was 29.0 to 43.3 years, and 93.5% had relapsing-remitting MS. Consistency DQIs showed a total of 2899 patients had achieved at least two years of follow-up; 6.9% did not have any recorded visits during this timeframe. Discrepant values were assessed as part of Accuracy DQIs, and improvements over time were noted for recorded dates of MS onset and diagnosis. Regarding Completeness DQIs, 191/5069 (3.8%) patients were lost to follow-up.<br></p><p>CONCLUSION: The application of 28 DQIs within the CLARION study has helped with understanding, not only intrinsic and question-specific determinants of data quality, but also tracking the quality of post-authorization safety data obtained from MS registries/data sources, thereby providing a foundation for the regulatory decision-making process.<br></p>