FDA experiences with a centralized statistical monitoring tool

• Published in: Journal of biopharmaceutical statistics Vol. 34; no. 6; pp. 986 - 992
• Main Authors: Wang, Xiaofeng (Tina), Schuette, Paul, Kam, Matilde
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 01.11.2024
• Subjects: Centralized statistical monitoring; clinical investigator site selection; Clinical Trials as Topic; Clinical Trials Data Monitoring Committees; data anomalies; Data Interpretation, Statistical; data quality/integrity; error detection; Female; Humans; Male; Pharmacology; Software; United States; United States Food and Drug Administration; United States; data anomalies; clinical investigator site selection; data quality/integrity; error detection; Centralized statistical monitoring
• DOI: 10.1080/10543406.2024.2330210
• ISSN: 1054-3406; 1520-5711

The U.S. Food and Drug Administration (FDA) has broadly supported quality by design initiatives for clinical trials - including monitoring and data validation - by releasing two related guidance documents (FDA 2013 and 2019). Centralized statistical monitoring (CSM) can be a component of a quality by design process. In this article, we describe our experience with a CSM platform as part of a Cooperative Research and Development Agreement between CluePoints and FDA. This agreement's approach to CSM is based on many statistical tests performed on all relevant subject-level data submitted to identify outlying sites. An overall data inconsistency score is calculated to assess the inconsistency of data from one site compared to data from all sites. Sites are ranked by the data inconsistency score ( $ - {\log _{10}}\left(p \right), $ − log 10 p , where $p$ p is an aggregated p-value). Results from a deidentified trial demonstrate the typical data anomaly findings through Statistical Monitoring Applied to Research Trials analyses. Sensitivity analyses were performed after excluding laboratory data and questionnaire data. Graphics from deidentified subject-level trial data illustrate abnormal data patterns. The analyses were performed by site, country/region, and patient separately. Key risk indicator analyses were conducted for the selected endpoints. Potential data anomalies and their possible causes are discussed. This data-driven approach can be effective and efficient in selecting sites that exhibit data anomalies and provides insights to statistical reviewers for conducting sensitivity analyses, subgroup analyses, and site by treatment effect explorations. Messy data, data failing to conform to standards, and other disruptions (e.g. the COVID-19 pandemic) can pose challenges.