CCDM Exam Dumps - Certified Clinical Data Manager

Before implementing anEDC system upgrade, thefirst taskof the Data Manager is toassess the impact on the data.

According to theGCDMP (Chapter: Electronic Data Capture Systems)andFDA 21 CFR Part 11, any system upgrade must undergoimpact assessmentto determine how the change might affectdata integrity, functionality, validation, and ongoing study operations. This assessment ensures that no data are lost, corrupted, or rendered inconsistent during or after the upgrade.

The Data Manager should evaluate:

Potential effects on existing data, edit checks, and reports,

System functionality impacting current workflows, and

Any revalidation requirements.

Only after the impact is understood should the Data Manager proceed to communicate with sites (option A), update documentation (option B), or modify CRFs if required (option D).

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture Systems, Section 7.3 – System Upgrades and Change Control

FDA 21 CFR Part 11 – Change Control and Validation Requirements

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Change Impact on Data Integrity and System Validation

UnderFDA 21 CFR Part 11, organizations using electronic systems must ensure thatall system users are trainedto perform their assigned functions before gaining access to the system. The regulation requires documented evidence of training but does not specify how it should be conducted (e.g., exam-based, in person, or language-specific).

TheGCDMP (Chapter: Computerized Systems and Compliance)further clarifies that personnel training should include instruction onsystem functionality, audit trails, data entry procedures, and electronic signaturesto maintain compliance and data integrity. Training must beperformed and documentedbut does not require a specific format or delivery method.

Therefore,option A—Training must be performed—is correct, as it reflects theminimum regulatory expectationper FDA and SCDM standards.

Reference (CCDM-Verified Sources):

FDA 21 CFR Part 11, Section 11.10(i) – Personnel Training Requirements

SCDM GCDMP, Chapter: Computerized Systems and Compliance, Section 5.4 – System Training and Documentation

ICH E6(R2) GCP, Section 2.8 – Qualified Personnel and Training Requirements

Aprotocol compliance edit checkis designed to ensure that the data collected adheres to thespecific requirements defined in the study protocol, such as visit timing, procedure windows, and eligibility criteria.

The example inoption A— an edit check that triggers when a visit date falls outside the protocol-specified window — directly verifies compliance with the study design. This type of check supports real-time monitoring of protocol adherence, a critical quality and regulatory requirement underGCDMPandICH E6(R2).

Other options are examples of generaldata validation checks, not protocol compliance:

B:Ensures clinical plausibility (data range check).

C:Ensures completeness (missing data check).

D:Ensures format correctness (system validation check).

Thus,option Abest represents aprotocol compliance edit check, confirming that collected data conform to the visit schedule defined in the protocol.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 5.4 – Protocol Compliance Edit Checks

ICH E6(R2) GCP, Section 5.1.1 – Quality Management and Compliance Controls

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – Edit Check Design and Validation

This question tests workload estimation and resource planning, which are fundamental competencies outlined in the Good Clinical Data Management Practices (GCDMP, Chapter on Project Management in Data Management). The task is to determine the Data Manager effort required based on the frequency and duration of data collection and processing activities.

Let’s calculate step by step:

Number of patients: 100

Frequency: Weekly (once per week)

Duration: 6 months ≈ 26 weeks

Time per file: 20 minutes

Total time per week:

100 patients × 20 minutes = 2,000 minutes per week

= 2,000 ÷ 60 = 33.3 hours per week

Total hours over 6 months:

33.3 hours/week × 26 weeks = 866 hours total

A full-time Data Manager typically works ~160 hours per month, so over six months:

160 × 6 = 960 hours total full-time capacity.

Therefore, the workload of 866 hours is approximately equivalent to one full-time Data Manager working across the six-month period:

866 ÷ 960 ≈ 0.9 FTE (Full-Time Equivalent).

This aligns most closely with Option D: One Data Manager per month (i.e., a full-time resource is required throughout the duration of the trial).

According to the GCDMP Project Management chapter, accurate resource estimation is critical in ensuring data management timelines are met without overloading staff or compromising data quality. The estimation process must consider not just the raw data download time but also associated data processing, verification, and upload into the clinical database.

Other options underestimate the effort significantly:

A (10%) and B (50%) do not account for cumulative weekly workload across multiple patients.

C (Two Data Managers) overestimates, as one Data Manager working full-time can manage the load efficiently.

Therefore, Option D is correct — approximately one full-time Data Manager (1.0 FTE) is required for the activity data alone during the six-month trial.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Project Management in Data Management, Section 5.3 – Workload Estimation and Resource Allocation

SCDM GCDMP, Chapter: Data Handling and Processing – Effort Estimation for Repetitive Data Tasks

ICH E6 (R2) Good Clinical Practice, Section 5.1 – Quality Management and Resource Planning

FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 4.3 – Operational Considerations for Data Management Activities

The best method for identifying inaccuracies in safety data tables prepared for aNew Drug Application (NDA)is tocompare counts of appropriate patients from line listings of CRF data to the counts in table cells.

According to theGCDMP (Chapter: Data Quality Assurance and Control), line listings representraw, patient-level dataextracted directly from the clinical database, whereas summary tables areaggregated outputsused for reporting and submission. Comparing these two sources ensuresdata traceability and accuracy, verifying that tabulated results correctly reflect the underlying patient data.

Manual CRF checks (option A) are less efficient and error-prone, as data entry is typically already validated electronically. Simply reviewing tables or listings for “odd values” (options C and D) lacks the systematic verification necessary for regulatory data integrity.

Thus,comparing line listings to tables (option B)provides a quantitative cross-check between the database and output deliverables, a standard practice in NDA data validation and statistical quality control.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 5.2 – Validation of Tables, Listings, and Figures (TLFs)

FDA Guidance for Industry: Submission of NDA Safety Data, Section on Data Verification and Accuracy

ICH E6 (R2) GCP, Section 5.5.3 – Validation of Derived Data Outputs

When aprotocol is amended mid-study, resulting in additional data collection requirements, theData Management Plan (DMP)must beupdated accordinglyand all relevant stakeholders must benotified.

According to theGCDMP (Chapter: Data Management Planning and Study Start-up), the DMP is aliving documentthat defines all data management processes for a clinical study. It must accurately reflect thecurrent data flow, CRF design, validation procedures, and reporting structure. Any protocol amendments affecting data capture, structure, or analysis require immediate DMP revision and distribution to ensure alignment across data management, clinical, and biostatistics teams.

Failure to update and communicate DMP changes can lead to misalignment in data handling and introduce compliance risks during audits or inspections. Therefore,Option Bis correct: the DMP must be updated and the change communicated to all stakeholders (e.g., sponsor, CRO, clinical operations, biostatistics).

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Plan (DMP), Section 5.3 – Maintaining and Updating the DMP

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Documentation of Protocol Changes and Data Handling Procedures

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Section on Data Management Documentation

The most effective method forquality control (QC)of the query resolution process is toperform random audits of resolved query forms. This ensures that queries are being appropriately raised, addressed, and resolved in accordance with the study protocol, data management plan (DMP), and standard operating procedures (SOPs).

According to theGCDMP (Chapter: Data Validation and Cleaning), QC activities should verify that the data review and query management process maintains high accuracy and consistency. Random auditing of resolved queries enables verification that:

Queries were raised for legitimate discrepancies,

The site’s responses were appropriate, and

The resolution actions taken by data management were correct and well-documented.

Metrics such as turnaround time (options A and C) or query counts (option B) measure efficiency butdo not assess quality. True quality control focuses on ensuring that data corrections preserve accuracy, auditability, and traceability — the fundamental principles ofdata integrityin clinical research.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 5.4 – Query Management and Quality Control

ICH E6 (R2) GCP, Section 5.5.3 – Data Integrity and Validation Procedures

According toICH E6(R2) Good Clinical Practice (GCP), Section 5.18.1, theSponsoris ultimatelyresponsible for developing and implementing the Monitoring Plan.

The Monitoring Plan defines:

Theextent and nature of monitoring(e.g., on-site, remote, risk-based).

Theresponsibilities of monitors.

Thecommunication and escalation proceduresfor data quality and protocol compliance.

While theCRO (B)orMonitor (D)may perform monitoring activities under delegation, theSponsorretains legal accountability for ensuring a compliant and effective plan is developed and maintained. TheData Manager (C)may contribute by outlining data review workflows, but is not responsible for authoring or owning the plan.

Therefore,option A (Sponsor)is the correct answer.

Reference (CCDM-Verified Sources):

ICH E6(R2) GCP, Section 5.18.1 – Purpose and Responsibilities for Monitoring

SCDM GCDMP, Chapter: Regulatory Compliance and Oversight, Section 5.3 – Sponsor Responsibilities in Monitoring and Quality Assurance

FDA Guidance for Industry: Oversight of Clinical Investigations – Sponsor Responsibilities (2013)