Last Attempt CCDM Questions

Aself-evident correction (SEC)refers to a data correction that is obvious, logical, and unambiguous — such as correcting an impossible date (e.g., 31-APR-2024) or standardizing a known abbreviation (e.g., “BP” to “Blood Pressure”). According to theGood Clinical Data Management Practices (GCDMP), SECs can be applied by data management stafffollowing pre-approved conventions defined in the Data Management Plan (DMP).

The DMP should explicitly describe the criteria for SECs, including the types of errors eligible for this correction method, the required documentation, and the communication procedure to inform the investigative site. The process must maintainaudit trail transparencyand ensure that all changes aretraceable and justified.

Options A and B suggest unauthorized or informal change procedures, which violate audit and compliance standards. Option C is too restrictive, as it prevents the efficient correction of non-clinical transcription or formatting errors.

Therefore,option Dis correct:“Self-evident changes may be made per the listed conventions and documented to the investigative site.”This approach aligns with CCDM expectations for balancing efficiency, accuracy, and regulatory compliance.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 6.2 – Self-Evident Corrections

FDA 21 CFR Part 11 – Electronic Records; Audit Trails and Traceability Requirements

The most critical consideration is thatdata cleaning is an iterative process, and completing all necessary steps — includingquery generation, site resolution, and second-pass validation— cannot realistically be accomplished within two weeks after study close.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning), data cleaning must occurcontinuously throughout the study, not only at the end. Post-database lock activities typically include running final validation checks, resolving outstanding queries, performing reconciliation (e.g., SAEs, labs, coding), and conducting final quality review.

Even in small studies,query turnaround and response cyclesfrom sites take time — typically2–4 weeks per iteration— making a two-week total cleaning period unrealistic.

Therefore,Option Dis correct: it would take more than two weeks to handle second-round (follow-up) queries and confirm final resolutions prior to database lock.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 5.4 – Ongoing vs. End-of-Study Data Cleaning

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Quality and Timeliness

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Management and Cleaning

Thebest methodto identify sites withhigh subject attritionis to calculate theproportion of patients for which two visit periods have passed without data, by site.

According to theGCDMP (Chapter: Data Quality Assurance and Control), subject attrition is an important performance indicator for data completeness and site compliance. Evaluating missing or delayed data acrossmultiple consecutive visit periodsallows for early detection of potential dropouts or site-level operational issues.

By assessing this proportion at thesite level, the Data Manager can distinguish between random missing data and systematic site underperformance. Counting or proportioning late visits (options B and C) identifies scheduling delays, not attrition. Looking at missing data without site context (option D) fails to identify site-specific patterns, limiting corrective action.

This metric aligns withrisk-based monitoring (RBM)practices recommended byICH E6 (R2)andFDA RBM Guidance, which promote proactive identification of sites at risk of data loss.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 5.4 – Site Performance Metrics

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Monitoring and Site Performance Evaluation

FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section 6 – Site Performance Metrics

The most effective information for addressinga backlog of unresolved queriesat investigative sites is alist of late queries by site combined with a summary table.

According to theGCDMP (Chapter: Communication and Issue Escalation), timely and structured feedback to sites is critical for efficient query resolution. A detailed list oflate or overdue queries, accompanied by summary statistics (e.g., counts, durations, status), enables data managers and monitors to prioritize follow-up actions, target problem areas, and provide focused support or retraining to underperforming sites.

While query count summaries (option B) are helpful for overview metrics, they lack the specific information (query ID, date, field, status) required for targeted follow-up. Graphs of enrollment or clean cases (options A and C) are unrelated to discrepancy resolution performance.

Thus, the combination ofdetailed lists and summarized performance metricsoffers both granularity and a high-level overview — the optimal tool for query management communication.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Communication and Issue Escalation, Section 5.1 – Site Query Management Reports

ICH E6 (R2) GCP, Section 5.18.4 – Communication Between Monitors and Sites

FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section on Query Metrics and Site Performance Review