CCDM Exam Dumps - SCDM Clinical Data Management Questions and Answers

TheData Manager (DM)plays a critical role in maintaining transparent communication with the clinical study team regardingdata quality and study progress. One of the most essential metrics regularly reported by the DM is thepercentage of data entered and cleaned.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Communication and Study Reporting), these metrics provide insight into study status, data readiness for interim analysis, and timeline predictability for database lock. Regular communication includes:

Percent of CRFs entered and verified

Percent of queries resolved

Outstanding data issues or missing pages

Other options fall outside the Data Manager’s direct responsibility:

A (Enrollment)is typically reported by clinical operations.

B (Staffing changes)are handled by site management.

D (Safety events)are communicated by the safety/pharmacovigilance team.

Thus,option Ccorrectly reflects the Data Manager’s responsibility for ongoing study communication.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Communication and Study Reporting, Section 5.3 – Study Metrics and Status Updates

ICH E6(R2) GCP, Section 5.1.1 – Communication and Oversight in Quality Management

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Data Status Reporting

When initially assessingdata cleanlinessin preparation for aninterim analysis, the focus should be onoutstanding issuesthat could affect data completeness and reliability.

According to theGCDMP (Chapter: Data Quality Assurance and Control), key indicators of readiness include:

TheCRF data entry statusof received pages (option A) to confirm completeness.

Identification ofmissing pages or visits(option B) to verify subject-level completeness.

A listing ofoutstanding discrepancies and their aging(option D) to assess unresolved data issues.

Counting the number ofdiscrepancies resolved to date (option C), however, does not reflect data quality or current data readiness—it indicates past actions rather than current unresolved risks. Therefore, it isnot a valid measurefor assessing interim data cleanliness.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 6.1 – Data Readiness Assessments for Analysis

ICH E6 (R2) GCP, Section 5.18.4 – Ongoing Data Quality Review

FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section 7 – Data Quality Indicators

In this scenario, the site hasdeviated from the approved study protocolby using adifferent formulation (nebulized albuterol instead of inhaler). This is considered aprotocol deviation or violation, depending on study definitions.

PerGCDMP (Chapter: Data Validation and Cleaning)andICH E6(R2), Data Managers are responsible for ensuring that all protocol deviations affecting data integrity or subject safety areaccurately captured and documentedwithin the clinical database. The appropriate action is toissue a data queryprompting the site to record the deviation in the designated section (e.g., “Protocol Deviations” CRF).

Option A:Incorrect — it affects data comparability.

Option B:Escalation to the Ethics Committee is handled by the sponsor, not the Data Manager.

Option C:Updating the CRF guidelines is premature; first, the deviation must be logged and assessed.

Therefore,option D (Query the site to enter a Protocol Violation)is the correct and compliant action.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 6.2 – Query Management and Protocol Deviations

ICH E6(R2) GCP, Section 4.5 – Compliance with Protocol

FDA Guidance for Industry: Oversight of Clinical Investigations — Compliance and Protocol Deviation Reporting

The efficacy endpoint ofall-cause mortality at one yeardirectly depends on thedate of deathfor each subject, makingOption D – Date of deaththe required data element.

According to theGCDMP (Chapter: Clinical Trial Protocols and Data Planning)andICH E3/E9 Guidelines, the primary efficacy analysis must be based on time-to-event data, particularly when the endpoint involvesmortality or survival. Thedate of deathallows accurate calculation oftime from randomization to event, essential for survival analysis (e.g., Kaplan-Meier curves).

Whilecause of death (C)may be collected for safety or secondary analyses,all-cause mortalityspecifically includes any death regardless of cause.Drug levels (A)andcoagulation times (B)may serve as pharmacodynamic or exploratory endpoints but do not directly measure mortality.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Protocol Review, Section 5.4 – Defining Data Required for Endpoints

ICH E9 – Statistical Principles for Clinical Trials, Section 2.3 – Time-to-Event Endpoints

FDA Guidance for Industry: Clinical Trial Endpoints for Drug Development and Approval

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

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

In adouble-blind clinical trial, therandomization schedulemust be generated by anindependent biostatistician not directly involved in study operations or data managementto preserve study blinding and integrity.

According toICH E9and theGCDMP (Chapter: Regulatory Requirements and Compliance),randomization generation and blindingmust be handled in a way that prevents bias or unintentional unblinding of study personnel. Thesponsor’s biostatistician not assigned to the project (Option C)is the appropriate person because they have the necessary statistical expertise but remain operationally independent from study execution.

Aproject biostatistician (Option D)orprogrammer (Option A)directly involved in data analysis could inadvertently compromise blinding. TheCRO biostatistician (Option B)should not perform this function if the sponsor retains randomization responsibility.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Regulatory Requirements and Compliance, Section 6.4 – Randomization and Blinding

ICH E9 – Statistical Principles for Clinical Trials, Section 5.4 – Randomization Procedures and Blinding

FDA Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics, Section 4.3 – Maintaining Blinding Integrity

When subjects are numberedsequentially within each site, it means that thesubject identification numbers (Subject IDs)restart from 001 at each site. For example, Site 101 may have Subject 001, and Site 102 may also have a Subject 001. In such cases, thesubject number alone is not globally uniqueacross the entire study. Therefore, when integrating or joining data across multiple database tables (for example, linking demographic, adverse event, and laboratory data), both thesite number and the subject numberare required to create a unique key that accurately identifies each record.

According to theGood Clinical Data Management Practices (GCDMP, Chapter on CRF Design and Data Collection), every data record in a clinical trial database must be uniquely and unambiguously identified. This is typically achieved through acomposite key, combining identifiers such assite number,subject number, and sometimesstudy number. The GCDMP specifies that a robust data structure must prevent duplication or mislinking of records across domains or tables.

Furthermore,FDA and CDISC standards (SDTM model)also emphasize the importance ofunique subject identifiers (USUBJID), which are derived from concatenating the study ID, site ID, and subject ID. This ensures traceability, integrity, and accuracy of subject-level data during database joins, data exports, and regulatory submissions.

Thus, in the described scenario, since subject numbering restarts at each site,both the site number and subject numberare required to uniquely identify and correctly join subject data across different datasets or tables.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 4.1 – Unique Subject Identification

CDISC SDTM Implementation Guide, Section 5.2 – Subject and Site Identification (Variable: USUBJID)

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – Data Integrity and Record Identification

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

In studies utilizingElectronic Data Capture (EDC)systems, many traditional paper-based processes such astracking and retrieving CRFsare eliminated or automated. However,query management and resolutionremain essential because discrepancies, missing data, and protocol deviations still require clarification and correction, regardless of the data collection medium.

According to theGCDMP (Chapter: Data Validation and Cleaning),data queriesare generated automatically or manually when inconsistencies are detected by edit checks. Sites must still respond to these queries electronically to ensure the integrity and completeness of data.

AandDare obsolete with EDC (no physical CRFs).

Brefers to manual data entry updates, which are replaced by direct EDC entry.

C (Resolving queries)continues as a key part of the data management workflow, even in fully electronic environments.

Thus,option Cis correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 5.4 – Query Generation and Resolution in EDC Systems

ICH E6(R2) GCP, Section 5.5.3 – Data Review and Query Resolution Requirements

FDA 21 CFR Part 11 – Electronic Records: Audit Trails and Query Documentation

C