IMPALA Consortium is pleased to announce the release of its newest open-source R Package, the Clinical Trial Anomaly Spotter (CTAS.) CTAS is a powerful tool for Central Statistical Monitoring that identifies outliers and anomalies efficiently and accurately in clinical trial time series. Its main focus is on flagging sites with one or more study parameters whose profiles differ from those of the other sites. In addition, the results can be used to identify anomalies for individual subjects.
Development of the package was spearheaded by Pekka Tiikkainen, Principal Clinical Data Scientist at Bayer, and tested and adapted for user flexibility across organizations by members of IMPALA’s Anomaly Detection in Clinical Data Work Product Team. The underlying algorithm of the CTAS operates by defining one or more time series for each parameter. The algorithm summarizes time series into a set of features such as Mean, Standard deviation, Unique value count, and Autocorrelation. These features help in identifying individual subjects with suspicious data.
CTAS represents a significant advancement in clinical trial data analysis; however, IMPALA’s vision for CTAS extends beyond its current capabilities and usage. CTAS has the potential to be an industry-standard tool that can significantly enhance the integrity and reliability of clinical trial data, leading to more accurate research outcomes and ultimately, better patient care.
IMPALA proudly invites all interested partners to test, utilize, and provide feedback for this innovative package.
The CTAS R package is freely available at https://github.com/IMPALA-Consortium/ctas
