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Background <p>Current ICH guidelines, e.g.&#xa0;ICH E6 (R3), advocate a risk-based statistical review of clinical trial data to identify anomalies. The open-source R package, clinical trial anomaly spotter (CTAS) has been developed by Bayer and the Intercompany Quality Analytics (IMPALA) consortium, helps detect inconsistencies in subject time series data at both site and subject levels, facilitating timely intervention.</p> Methods <p>CTAS analyzes time series of equal length. Each subject-level time series is summarized as six optional scalars: mean, standard deviation, range, relative unique value count, autocorrelation and local outlier factor. To detect site-level anomalies, sites can be scored using 3 different scoring methods. The performance of the CTAS algorithm was tested using simulations, artificially introducing site anomalies of various types and degrees into clinical trial data sets.</p> Results <p>We found that CTAS can reliably detect site anomalies depending on the degree of the anomaly introduced. Less complex anomalies such as mean were easier to detect than complex outlier such as local outlier factor. The three scoring methods differed in their ability to detect anomalous sites with a small number of patients and their false positive rates.</p> Conclusions <p>CTAS is a valuable tool for timely detection of outliers in clinical data, suitable for integration into risk-based strategies. Choosing the appropriate site anomaly scoring method is crucial for handling sites with fewer subjects effectively.</p>

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Enhancing Data Quality in Clinical Trials: Cross-Company Validation of the Open-Source Clinical Trial Anomaly Spotter (CTAS)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月12日

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  • Pekka Tiikkainen,
  • Frederik Collin,
  • Björn Koneswarakantha

摘要

Background

Current ICH guidelines, e.g. ICH E6 (R3), advocate a risk-based statistical review of clinical trial data to identify anomalies. The open-source R package, clinical trial anomaly spotter (CTAS) has been developed by Bayer and the Intercompany Quality Analytics (IMPALA) consortium, helps detect inconsistencies in subject time series data at both site and subject levels, facilitating timely intervention.

Methods

CTAS analyzes time series of equal length. Each subject-level time series is summarized as six optional scalars: mean, standard deviation, range, relative unique value count, autocorrelation and local outlier factor. To detect site-level anomalies, sites can be scored using 3 different scoring methods. The performance of the CTAS algorithm was tested using simulations, artificially introducing site anomalies of various types and degrees into clinical trial data sets.

Results

We found that CTAS can reliably detect site anomalies depending on the degree of the anomaly introduced. Less complex anomalies such as mean were easier to detect than complex outlier such as local outlier factor. The three scoring methods differed in their ability to detect anomalous sites with a small number of patients and their false positive rates.

Conclusions

CTAS is a valuable tool for timely detection of outliers in clinical data, suitable for integration into risk-based strategies. Choosing the appropriate site anomaly scoring method is crucial for handling sites with fewer subjects effectively.