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Which metrics report listed below would best help identify trends in the clinical data?
The Query frequency counts per data element (Option D) is the best metric for identifying data trends and potential systemic data issues in clinical trials.
According to the Good Clinical Data Management Practices (GCDMP, Chapter: Data Quality Assurance and Control), trend analysis involves identifying recurring data issues across subjects, sites, or variables to detect training gaps, protocol misinterpretation, or CRF design flaws. A high number of queries generated for specific fields (e.g., visit date, lab values, or dosing information) may indicate systemic problems such as unclear CRF instructions or site-level misunderstandings.
While metrics such as percent of data cleaned (A) and time to database lock (B) reflect overall progress and efficiency, they do not identify specific data pattern issues. The number of subjects screened/enrolled (C) pertains to recruitment rather than data quality.
Therefore, query frequency per data element provides actionable insights for quality improvement, process refinement, and early identification of potential risks.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 6.3 -- Metrics and Trend Analysis
ICH E6 (R2) Good Clinical Practice, Section 5.18.4 -- Risk-Based Quality Review and Data Trends
FDA Guidance for Industry: Oversight of Clinical Investigations -- Risk-Based Monitoring, Section 6 -- Data Metrics and Trend Evaluation