CCDM Certified Clinical Data Manager Questions and Answers

A protocol compliance edit check is designed to ensure that the data collected adheres to the specific requirements defined in the study protocol , such as visit timing, procedure windows, and eligibility criteria.

The example in option 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 under GCDMP and ICH E6(R2) .

Other options are examples of general data 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 A best represents a protocol 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

In a randomized, double-blind, placebo-controlled study , if statistical analysis shows that the placebo appears to outperform the investigational product , a likely cause is a data management or coding error , particularly in treatment code entry or mapping .

According to the GCDMP (Chapter: Database Design and Build) , treatment assignment data — typically stored in randomization or code-break files — must be accurately integrated into the clinical database. Any mismatch between randomization codes, subject identifiers, or treatment arms can lead to incorrect grouping during analysis, producing false conclusions such as placebo superiority.

The Data Manager should initiate a root cause review of randomization data integration and treatment mapping. The placebo is never designed to have active medicinal effects (option A). Option D is incorrect because the described scenario implies a data inconsistency, not true efficacy differences. Proper verification of randomization coding and reconciliation between data management and statistical programming systems are essential.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Build, Section 6.1 – Randomization and Treatment Code Management

ICH E6 (R2) GCP, Section 5.5.3 – Data Verification and Coding Accuracy

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Mapping and Validation Requirements

According to ICH E6(R2) Good Clinical Practice (GCP) , Section 5.18.1, the Sponsor is ultimately responsible for developing and implementing the Monitoring Plan .

The Monitoring Plan defines:

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

The responsibilities of monitors .

The communication and escalation procedures for data quality and protocol compliance.

While the CRO (B) or Monitor (D) may perform monitoring activities under delegation, the Sponsor retains legal accountability for ensuring a compliant and effective plan is developed and maintained. The Data 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)

To identify site variability in eligibility screening , the most effective approach is to plot eligibility rate by site . This allows visual detection of differences in how well each site screens subjects according to protocol-defined inclusion and exclusion criteria.

The GCDMP (Chapter: Data Quality Assurance and Metrics) emphasizes the importance of graphical analysis for identifying anomalies and site-level performance variability. By plotting the eligibility rate by site, data managers and clinical operations teams can quickly identify outliers—sites that screen too many or too few eligible subjects—indicating potential training issues, misunderstanding of inclusion/exclusion criteria, or even possible protocol deviations.

While summarizing screening rate (B) provides useful numeric data, it lacks visual comparability. Listing waivers (A) or enrollment counts (C) provide limited insights into eligibility consistency .

Therefore, option D — Plot eligibility rate by site —is the best analytic and visualization practice to assess site variability in screening outcomes.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Control, Section 6.1 – Use of Metrics and Graphical Review for Site Performance

ICH E6(R2) GCP, Section 5.18.4 – Identification of Systematic or Site-Specific Issues

In the context of database design and external data management , a test data transfer (or trial data load) is performed to ensure the proper configuration, structure, and integrity of data imported from an external vendor or system. The second test transfer is specifically useful to confirm that data structures and formats are consistently aligned between the sending and receiving systems after initial adjustments have been made from the first test.

According to the Good Clinical Data Management Practices (GCDMP) , the file format — including variables, data types, field lengths, delimiters, and encoding — must be validated during test transfers to confirm compatibility and ensure accurate loading into the target database. Once the initial test identifies and corrects errors (e.g., mismatched variable names or data types), the second transfer verifies that the corrections have been implemented correctly and that the file structure functions as intended.

Testing change management (A) involves procedural controls, not data transfers. The transfer method (C) and transfer frequency (D) are validated during initial process setup, not during subsequent test transfers.

Therefore, option B (File format) is correct, as the second test transfer verifies the technical integrity of the file structure before live production transfers begin.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: External Data Transfers and Data Integration, Section 5.2 – Test Transfers and File Validation

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – Data Import and Validation Controls

In Electronic Data Capture (EDC) systems, free text fields should be used only when a predefined list of acceptable responses cannot accommodate the full variability of input data — most notably for Adverse Event (AE) verbatim terms .

According to the Good Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection) , AE verbatim terms are initially entered as free text by site staff to accurately capture the investigator’s exact medical description of the event. These verbatim terms are later coded using standardized dictionaries such as MedDRA during medical coding, ensuring both flexibility and standardization in reporting.

Conversely, fields such as urine sedimentation rate (A) , date of birth (C) , and Body Mass Index (D) require structured numeric or date formats to enable validation, range checks, and consistency across datasets. Free text would compromise data integrity, accuracy, and validation efficiency for these structured data elements.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 4.3 – Use of Free Text and Coded Fields

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Structure and Validation

MedDRA Introductory Guide, Section 2.3 – Verbatim Entry and Coding Requirements

The Investigator holds the primary responsibility for ensuring the accuracy, completeness, and timeliness of Case Report Form (CRF) entries. This responsibility is mandated by regulatory requirements under ICH E6(R2) Good Clinical Practice (GCP) .

The investigator may delegate CRF completion to a qualified designee (e.g., site coordinator) , but the ultimate accountability remains with the investigator. The investigator’s signature (electronic or manual) on the CRF serves as certification that the data accurately reflect the source documents and the patient’s participation.

The GCDMP (Chapter: CRF Design and Data Collection) reinforces this by stating that while data managers ensure design quality and CRAs verify consistency with source data, the investigator is legally responsible for CRF accuracy .

Thus, option D (Investigator) is correct, as it aligns with both GCP and CCDM standards.

Reference (CCDM-Verified Sources):

ICH E6(R2) GCP, Section 4.9 – Records and Reports (Investigator Responsibilities)

SCDM GCDMP, Chapter: CRF Design and Data Collection, Section 5.1 – Investigator’s Role in Data Accuracy

FDA 21 CFR Part 312.62 – Investigator Recordkeeping and Record Retention

In an ophthalmology clinical study , data criticality is determined by how directly a data element affects safety evaluation , efficacy assessment , and regulatory decision-making . According to the Good Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning) , critical data fields are those that:

Have a direct impact on the primary and secondary endpoints , or

Are essential for safety interpretation and adverse event causality assessment .

Among the listed options, Concomitant Medications (Option B) are considered critical data for ophthalmology studies. This is because many ocular treatments and investigational products can interact with systemic or topical medications, potentially affecting ocular response , intraocular pressure , corneal healing , or visual function outcomes . Any inconsistency in concomitant medication data could directly influence safety conclusions or efficacy interpretations .

Other options, while important, are less critical for this study type:

Subject Identifier (A) is essential for data traceability and audit purposes but is not directly related to safety or efficacy outcomes.

Weight (C) may be relevant in dose-dependent drug trials but is rarely a pivotal variable in ophthalmology, where local administration (eye drops, intraocular injections) is common.

Medical History (D) provides contextual background but does not have the same immediate impact on endpoint analysis as current concomitant treatments that can confound the therapeutic effect or cause ocular adverse events.

Per GCDMP and ICH E6 (R2) GCP guidelines, data validation plans must define critical data fields during study setup, reflecting therapeutic area–specific priorities. For ophthalmology, concomitant medications, ocular assessments (visual acuity, intraocular pressure, retinal thickness, etc.), and adverse events are typically designated as critical fields requiring heightened validation, source verification, and reconciliation accuracy before database lock.

Thus, when QA identifies discrepancies between the CRF and source, the Concomitant Medications field (Option B) is the most critical to address immediately to ensure clinical and regulatory data integrity.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.4 – Critical Data Fields and Data Validation Prioritization

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Monitoring and Source Data Verification

FDA Guidance for Industry: Oversight of Clinical Investigations — A Risk-Based Approach to Monitoring, Section 5.3 – Identification of Critical Data and Processes

SCDM GCDMP Chapter: Data Quality Assurance and Control – Therapeutic Area–Specific Data Criticality Examples (Ophthalmology Studies)

When a CRF modification leads to a sudden increase in EDC queries , the most likely cause is an error or misconfiguration in the edit checks introduced during or after the change. Therefore, the first step should be to review the edit checks for correctness .

The GCDMP (Chapter: Database Design and Validation) emphasizes that any database or CRF modification should trigger retesting of affected validation rules . Incorrect logic, thresholds, or missing conditional statements in automated edit checks can cause false or redundant queries, leading to unnecessary data management burden and site frustration.

Manually handling edit checks (option A) or adding SDV (option B) does not address the root cause. Having monitors close queries (option D) would mask the problem rather than resolve it.

Thus, the correct corrective measure is Option C — review and validate the edit checks to ensure proper functionality.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Validation, Section 5.5 – Edit Check Testing and Review

ICH E6 (R2) GCP, Section 5.5.3 – Validation and Change Control for Electronic Systems

FDA 21 CFR Part 11 – System Validation and Change Documentation

The most significant difference in the Data Manager’s role when using an Electronic Data Capture (EDC) system is that data updates are implemented directly by site personnel (Option A).

According to the GCDMP (Chapter: Electronic Data Capture Systems) , EDC technology shifts responsibility for data entry and correction from the sponsor or CRO to the investigator site, enabling real-time data entry and validation. This eliminates the need for double entry or remote data transcription, allowing Data Managers to focus on system validation, query management, and data quality oversight rather than physical data handling.

However, the EDC system still requires full validation (contrary to Option B). Metrics generation (Option C) and CRF tracking (Option D) are important but not unique to EDC-based workflows.

Thus, the correct answer is Option A – Data updates are implemented by the sites , reflecting the most fundamental operational shift introduced by EDC systems.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture (EDC) Systems, Section 4.1 – Role of the Data Manager in EDC

ICH E6 (R2) GCP, Section 5.5.3 – Electronic Data Entry and Responsibilities

FDA 21 CFR Part 11 – Electronic Records and Signatures: Data Entry Responsibilities

When derived or calculated variables (like Body Mass Index ) are created, it is essential to document the algorithm used and its version to ensure full data traceability and reproducibility.

According to GCDMP (Chapter: Database Design and Derived Data) , every derived field must include metadata describing:

The derivation algorithm (e.g., BMI = weight [kg] / height² [m²] )

The version of the algorithm (if updates or revisions occur)

Any associated data sources or transformation rules

This ensures consistent calculation across systems, prevents discrepancies during regulatory submissions, and aligns with FDA and CDISC documentation expectations.

Option B lacks version control, which is critical for traceability. Option C describes audit trail data (not derivation metadata), and option D refers to broader documentation, not specific algorithm traceability.

Hence, option A (Algorithm and algorithm version associated with the calculated value) is the correct and compliant answer.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Derived Data and Algorithms, Section 5.3 – Documentation and Metadata Requirements

ICH E6(R2) GCP, Section 5.5.3 – Derived Data and Validation Traceability

FDA Guidance for Industry: Providing Regulatory Submissions in Electronic Format – Data Definitions (Define.xml)

Even when all range and logic checks are successfully resolved, discrepancies may still exist between the clinical database and the source documents . This typically indicates an error in data abstraction or transcription , meaning that data were incorrectly entered or extracted from the source records during the data entry or verification process.

According to the Good Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning) , data validation rules such as range and logic checks are designed to identify inconsistencies, missing data, or out-of-range values within the database itself. However, they do not verify the accuracy of data entry against the original source documents — that responsibility falls under source data verification (SDV) , typically conducted by clinical monitors or auditors.

When an auditor detects discrepancies between source and database values after all edit checks have passed, the most probable explanation is that data were not transcribed correctly from the source , rather than a failure in programmed edit checks. This could occur due to human error during manual data entry, misinterpretation of the source document, or oversight during SDV.

Option C (Data were changed after checks were run) might occur in rare cases but would normally be documented in an audit trail per 21 CFR Part 11 and ICH E6 (R2) standards. Option B misinterprets the issue, since “logical and in range” values can still be incorrect relative to the source. Option A (Auditor error) is possible but statistically less likely, as source data verification follows strict, documented audit procedures.

Therefore, the most likely reason for such discrepancies is Option D: Data were not abstracted correctly from the source , emphasizing the importance of robust data entry training, dual data entry, and verification procedures.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.5 – Source Data Verification and Reconciliation

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Monitoring and Source Data Verification

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – Source Data Accuracy and Audit Trails

21 CFR Part 11 – Electronic Records and Electronic Signatures, Subpart B: Audit Trails and Record Accuracy

Enrolling subjects before the Electronic Data Capture (EDC) system is ready poses major data integrity and compliance risks . The primary issue is that data cannot be accurately captured, validated, or monitored without the system in place.

Per the GCDMP (Chapter: Data Management Planning and Study Start-up) , data collection systems must be fully validated, tested, and released before enrollment begins to ensure:

Real-time data entry and quality control

Proper tracking of adverse events (AEs/SAEs)

Audit trails and traceability for regulatory compliance

Option A highlights the most critical consequence — without an operational EDC, data collection and verification processes cannot occur, compromising data quality and study oversight.

While options B, C, and D may be partially true, they are secondary effects . The fundamental consideration is data capture capability and monitoring control, making option A correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Management Planning and Study Start-up, Section 4.2 – EDC Readiness and System Validation

ICH E6(R2) GCP, Section 5.5.3 – Computerized Systems Validation Before Use

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.1 – System Qualification Prior to Data Entry

In a SQL (Structured Query Language) database, the SELECT statement specifies which columns to display from a table. In this query, only three columns — Pt_ID , MRN , and SSN — are being selected from the patient table.

This means the resulting dataset will contain:

The same number of rows (records) as the original table (assuming no WHERE filter), and

Fewer columns than the full table.

In database terminology:

“Wider” refers to more columns (fields).

“Narrower” refers to fewer columns (fields).

Since this query retrieves only 3 columns (out of potentially many in the original table), the result set is narrower than the patient table , making option D correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 5.1 – Relational Databases and Query Logic

ICH E6(R2) GCP, Section 5.5.3 – Data Retrieval and Integrity Principles

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.4 – Database Query Controls

The Study Schedule of Events (SoE) section in the protocol is the most concise and comprehensive representation of the timing of data collection throughout a study.

According to the Good Clinical Data Management Practices (GCDMP, Chapter: Data Management Planning and Study Start-up) and ICH E6 (R2) GCP , the SoE outlines what assessments, procedures, and data collections occur at each study visit (e.g., screening, baseline, treatment visits, follow-up). This table is a foundational tool for CRF design , database structure , and edit-check development , ensuring alignment between the protocol and data management systems.

While the study endpoints section (A) defines what is measured, and the protocol synopsis (C) summarizes the design, only the schedule of events (B) specifies when data collection occurs for each parameter. The ICH essential documents (D) pertain to regulatory documentation, not study visit timing.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 4.1 – Using the Schedule of Events for Database Design

ICH E6 (R2) GCP, Section 6.3 – Trial Design and Schedule of Assessments

FDA Guidance for Industry: Protocol Design and Data Collection Standards

In the EDC database build process , the first logical specifications that require approval are the electronic Case Report Form (eCRF) fields .

According to the Good Clinical Data Management Practices (GCDMP, Chapter: Database Design and Build) , eCRF field specifications define what data elements are collected, their data types, permitted values, field lengths, and any associated metadata. Approval of these specifications forms the foundation for subsequent design components such as edit check programming , query management rules , and data validation logic .

Edit checks (B) are developed only after fields and structures are finalized.

Metric reports (C) and eCRF guidelines (D) are downstream documentation or tools, not logical specifications required at the build start.

Therefore, option A (eCRF fields) is correct, as their approval marks the first formal milestone in the EDC system development life cycle.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 4.2 – Logical Design and eCRF Field Specifications

ICH E6(R2) GCP, Section 5.5.3 – System Design and Validation Documentation

FDA 21 CFR Part 11 – System Validation and Documentation Controls

Effective site communication in data management relies on transparent reporting of pending issues such as open queries, missing data, and overdue updates . According to the Good Clinical Data Management Practices (GCDMP, Chapter: Communication and Metrics) , the list of outstanding data and queries by site provides a direct, actionable overview of what each site needs to address, supporting accountability and timely resolution.

This list typically includes subject identifiers , query types , dates generated , and status of resolution , allowing data managers to prioritize site follow-ups. Regular distribution of this report fosters efficient collaboration between the data management team, monitors, and site staff, ultimately improving database cleanliness and timeline adherence.

Options A and B reflect general study status but do not target data issue resolution. Option C pertains to user access oversight, not data progress. Hence, option D is the correct and most operationally relevant answer.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Communication and Metrics, Section 5.2 – Site Reporting and Query Management Metrics

ICH E6(R2) GCP, Section 5.18 – Site Oversight and Communication Requirements

In the context of an investigator-initiated trial (IIT) where Standard Operating Procedures (SOPs) are not available, the most appropriate and compliant approach is to develop a Data Management Plan (DMP) template and then create a study-specific DMP based on that template ( Option C ).

According to the Good Clinical Data Management Practices (GCDMP, Chapter on Data Management Planning and Study Start-up) , the DMP is the central document that defines all processes, responsibilities, systems, and quality controls related to data collection, processing, validation, and database management throughout the clinical study. The DMP serves as a formal framework for ensuring data integrity , traceability , and regulatory compliance , especially in the absence of established institutional SOPs.

While SOPs provide organizational-level standards, the DMP provides study-specific operational detail . In an investigator-initiated setting, researchers often lack institutional data management infrastructure, so the DMP must substitute for SOP guidance by detailing:

Data entry and validation procedures

Query management and resolution processes

CRF design and data flow specifications

Database design, backup, and security

Responsibilities of study personnel (investigator, data manager, statistician)

Quality control and audit trail practices

Option A (“Data handling should be documented in a DMP”) is correct in principle but incomplete—without a DMP template , there is no standardized format or consistency across studies.

Option B (developing full SOPs) is not practical for a single IIT; SOPs are organizational-level documents requiring longer development and approval cycles.

Option D (briefly describing data management in the protocol) is insufficient, as the protocol should reference data management activities but not serve as the operational manual for them.

Therefore, Option C provides the most comprehensive, regulatory-compliant, and practical solution—ensuring structured documentation of all data management activities while maintaining flexibility for investigator-led research.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 5.2 – Data Management Plan (DMP) Development and Maintenance

ICH E6 (R2) Good Clinical Practice, Section 5.1 – Quality Management and Documentation Requirements

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 4 – Data Management and Documentation Practices

SCDM GCDMP, Chapter: Project Management in Data Management – Study-Specific Documentation and Planning in Investigator-Initiated Trials

In paper-based trials , site staff (e.g., study coordinators) record data manually on paper Case Report Forms (CRFs), which are later transcribed by data entry personnel into an electronic database.

However, in EDC-based studies , the site coordinator is directly responsible for entering data into the EDC system . This eliminates the need for centralized double data entry and shortens data cleaning timelines.

The GCDMP (Chapter: Electronic Data Capture Systems) states that EDC systems shift certain tasks, including data entry, initial query response, and source verification preparation , to the site level. Yet, data entry remains the most significant additional responsibility compared to paper-based studies.

Option A (Query resolution) is performed in both EDC and paper-based systems.

Option C (Data curation) is typically a Data Management function.

Option D (Medical record abstraction) is part of source documentation, not specific to EDC.

Thus, option B (Data entry) is correct — it is the additional site coordinator duty unique to EDC environments.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Electronic Data Capture (EDC) Systems, Section 5.3 – Site Responsibilities and Workflow Changes

ICH E6(R2) GCP, Section 5.5.3 – Data Entry and Role Delegation in Computerized Systems

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.2 – Site-Level Data Entry Controls

Regulatory agencies such as the FDA and ICH require that electronic data be retained in a format that preserves audit trails and traceability .

While PDF images (option C) provide a static representation of data, they do not preserve the underlying audit trail (i.e., who changed what, when, and why). The ASCII data files with corresponding audit trails (option D) provide complete transparency and comply with 21 CFR Part 11 and GCDMP archival standards.

Option A (storing computers) is unnecessary and impractical, and Option B (paper source documents) are site records, not system archives.

Hence, option D is correct — ASCII data files with audit trails meet traceability and compliance standards.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.4 – Archival Formats and Audit Trail Retention

ICH E6(R2) GCP, Section 5.5.3 – Data Integrity, Audit Trails, and Record Retention

FDA 21 CFR Part 11 – Electronic Records; Audit Trail and Retention Requirements

When combining data from two datasets where one uses numeric codes (1 = Male, 2 = Female) and another uses text codes (M, F), each unique value in one dataset corresponds exactly to one unique value in the other.

This relationship is a one-to-one mapping , where each element in one dataset maps directly to a single corresponding element in the other.

1 → M

2 → F

Such mappings ensure consistent data harmonization during data integration and standardization phases, as outlined in the GCDMP (Chapter: Database Design and Integration) .

Many-to-one (C) mapping would occur if multiple values (e.g., “Male,” “M,” “Man”) mapped to a single standardized value, which isn’t the case here.

Thus, the mapping is one-to-one , ensuring precise correspondence between both representations of gender data.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 5.4 – Data Mapping and Harmonization

CDISC SDTM Implementation Guide, Section 5.2 – Controlled Terminology and Mapping Rules

ICH E6(R2) GCP, Section 5.5.3 – Data Integrity and Integration Principles

Once a clinical database is locked , it becomes read-only — no further data modifications can be made by any users, including site personnel. This ensures that the data are finalized, consistent, and auditable for statistical analysis and regulatory submission.

According to the GCDMP (Chapter: Database Lock and Archiving) , the lock process involves freezing the database to prevent accidental or unauthorized changes. After lock, access permissions are restricted, and all edit and update functions are disabled. If any corrections are required post-lock, the database must be unlocked under controlled procedures (with full audit trail documentation).

Thus, option C — The site study coordinator is not able to make the change — correctly reflects standard EDC functionality and regulatory compliance.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.2 – Database Lock Procedures and Controls

ICH E6(R2) GCP, Section 5.5.3 – Data Integrity and Audit Trail Requirements

FDA 21 CFR Part 11 – Controls for Electronic Records and System Lock Functions

Before database lock , the Data Manager must confirm that all collected data are complete, validated, and reconciled across systems. This includes:

Ensuring data completeness (B) — confirming all expected forms and data files have been received.

Verifying coded data (A) — ensuring no pending terms remain in coding dictionaries like MedDRA or WHO Drug.

Performing SAE reconciliation (C) — cross-checking the clinical database against the safety system for accuracy.

However, ensuring study close-out visits (D) is not a data management function ; it falls under clinical operations and monitoring responsibilities . While data management may review confirmation of site close-outs, the activity itself is not part of pre-database lock procedures.

Therefore, option D correctly identifies the exception—an activity outside the data manager’s direct scope of responsibility before database closure.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.3 – Pre-Lock Validation and Reconciliation Activities

ICH E6(R2) GCP, Section 5.5.3 – Data Handling and Quality Control Prior to Lock

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.1 – Database Management and Lock Procedures

A complete data element definition in clinical data management should include:

Name and clear description of the data element,

Data type (e.g., numeric, text, date),

Precision or scale (if numeric), and

Unit or dimensionality of measure (e.g., centimeters, inches).

In this example, while the data type (“measured physical quantity”) and precision (0.1) are defined, the unit of measurement (e.g., centimeters or inches) is missing. This omission leads to ambiguity and could cause serious discrepancies when comparing or analyzing measurements.

The GCDMP (Chapter: Database Design and Build) emphasizes that units and dimensionality must be explicitly defined and consistently applied in all CRFs, metadata dictionaries, and data transformations.

Thus, option D (Unit or dimensionality of measure) is correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 5.2 – Metadata and Data Element Definitions

CDISC CDASH Implementation Guide, Section 3.3 – Data Element Metadata Requirements

ICH E6(R2) GCP, Section 5.5.3 – Data Accuracy and Standardized Definitions

To improve efficiency and ensure consistency across multiple studies, the most effective infrastructure solution is to maintain a centralized library of standardized forms or screen modules (e.g., CRF/eCRF templates).

According to the Good Clinical Data Management Practices (GCDMP, Chapter: Database Design and Build) , using a form library allows reuse of validated data collection modules for commonly collected domains such as demographics, adverse events, and vital signs. This reduces database setup time, enhances uniformity in data definitions, and ensures alignment with standards such as CDISC CDASH and SDTM .

While adopting ODM (A) provides standardized data exchange and interoperability, it does not inherently reduce setup workload. Improving design processes (C) enhances efficiency but doesn’t guarantee consistency, and implementing controlled terminology (D) helps with coding standardization, not database structure.

Therefore, option B — maintaining a library of form or screen modules — provides the most direct and sustainable improvement for scalability and quality.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 5.3 – Use of Standard Libraries and Templates

CDISC CDASH Implementation Guide, Section 3.2 – Reusable CRF Modules and Standardization

ICH E6(R2) GCP, Section 5.5.3 – Standardization and Reuse in Data Collection Systems

The Informed Consent Form (ICF) is the document that explicitly describes what study subjects can expect regarding data disclosure, privacy, and confidentiality during and after participation in a clinical trial. According to ICH E6 (R2) Good Clinical Practice and FDA Human Subject Protection Regulations (21 CFR Parts 50 and 56) , participants must be fully informed about how their personal and clinical data will be collected, used, stored, and shared — both during the study and in any subsequent data-sharing or publication activities.

The GCDMP reiterates that clinical data managers must ensure that all data handling practices align with the privacy commitments made in the ICF. This includes compliance with data protection regulations such as HIPAA (in the U.S.) and GDPR (in the EU). The ICF defines the permissible scope of data use, ensuring ethical management and subject protection.

Documents like the protocol or data sharing plan may outline procedures and responsibilities but do not directly inform participants of their rights and data use expectations. Only the ICF is designed for that ethical communication purpose.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Ethics, Privacy, and Data Security

ICH E6 (R2) Good Clinical Practice, Sections 4.8.10 & 4.8.12

FDA 21 CFR Part 50 – Protection of Human Subjects, Informed Consent Requirements

This scenario describes a single-blind trial, in which only one party —typically the subject —is unaware of the treatment assignment, while the investigator or surgeon knows which intervention is being administered.

In this case, the surgeon receives instructions on which stent (test or control) to use, meaning they are aware of treatment allocation. However, the subject is blinded to which device is being implanted. This setup minimizes subject bias while maintaining procedural safety since the surgeon must know which product to use.

Double-blind (A): Neither subject nor investigator knows the treatment.

Open-label (B): Both subject and investigator know the treatment.

Cross-over (D): Each subject receives both treatments in different periods.

Thus, the correct answer is C. Single-blind , as only the participant remains blinded in this surgical device trial design.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Clinical Trial Phases and Protocols, Section 3.2 – Study Blinding and Randomization Concepts

ICH E6(R2) GCP, Section 1.10 – Definition of Blinding/Masking

FDA Guidance for Industry: Design Considerations for Pivotal Clinical Investigations for Medical Devices, Section 5.3 – Blinding in Device Studies

Before selecting an Electronic Data Capture (EDC) system for a clinical trial, it is essential to have a clear understanding of the functional requirements . This serves as the minimum prerequisite to guide system selection, ensuring that the EDC solution aligns with the protocol needs, data workflow, security requirements, and regulatory compliance.

According to the Good Clinical Data Management Practices (GCDMP, Chapter: Computerized Systems and Compliance) , functional requirements describe what the system must do—such as data entry capabilities, edit checks, query management, user roles, audit trails, and integration with external systems (e.g., labs, ePRO). This understanding allows sponsors and CROs to evaluate vendor systems effectively during the selection and qualification phase.

Other options:

B. Installation qualification and D. Validation plan occur after system selection.

C. Governance documentation supports operations but is not required before choosing the system.

Hence, option A is correct — the first and most essential prerequisite before EDC selection is a solid understanding of the functional requirements .

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Computerized Systems and Compliance, Section 4.2 – Requirements Gathering and System Selection

FDA 21 CFR Part 11 – System Validation and Intended Use Requirements

ICH E6(R2) GCP, Section 5.5.3 – Computerized System Selection and Qualification

Adding multiple new sites late in the enrollment period creates a concentrated influx of new data near the end of the study. These sites typically start enrolling patients later, resulting in a “bolus” of Case Report Forms (CRFs) that must be entered, validated, and cleaned within a shorter timeframe to meet database lock deadlines.

According to the Good Clinical Data Management Practices (GCDMP, Chapter: Project Management and Data Flow) , late site activation compresses the timeline for data management tasks, necessitating increased resources for data entry, query management, and cleaning . Data management teams must anticipate this surge and plan accordingly—either by increasing staffing or revising timelines to prevent bottlenecks and maintain quality.

While option D (increased query rates) can occur, it is a secondary effect. The most direct and consistent impact is the surge in data volume requiring expedited processing near study end.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Project Management, Section 5.3 – Managing Changes in Site Activation and Data Flow

ICH E6(R2) GCP, Section 5.1 – Quality Management and Oversight

When integrating external environmental data such as air quality readings with clinical study data, it is essential to use location and time identifiers to properly align the environmental data with subject-level data.

According to the Good Clinical Data Management Practices (GCDMP, Chapter: Data Management Planning and Study Start-up) , external data sources (like national weather or pollution databases) must be merged using common linkage variables that allow synchronization without breaching subject confidentiality. In this case:

Location identifiers (e.g., city, postal code, or region) align the subject’s study site or residential area with the environmental dataset.

Time identifiers (e.g., date and time of data collection) ensure that the environmental readings correspond to the same period as the subject’s clinical observations.

Including subject identifiers (option C or D) is unnecessary and would pose privacy and data protection risks . Instead, linkage is typically done at the aggregate (site or regional) level , maintaining compliance with HIPAA and GDPR .

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Integration and External Data Handling, Section 4.3 – Linking External Data Sources

ICH E6 (R2) GCP, Section 5.5.3 – Data Traceability and External Data Management

FDA Guidance for Industry: Use of Electronic Health Record Data in Clinical Investigations, Section 5.2 – Linking and Integration Principles

The efficacy endpoint of all-cause mortality at one year directly depends on the date of death for each subject, making Option D – Date of death the required data element.

According to the GCDMP (Chapter: Clinical Trial Protocols and Data Planning) and ICH E3/E9 Guidelines , the primary efficacy analysis must be based on time-to-event data, particularly when the endpoint involves mortality or survival . The date of death allows accurate calculation of time from randomization to event , essential for survival analysis (e.g., Kaplan-Meier curves).

While cause of death (C) may be collected for safety or secondary analyses, all-cause mortality specifically includes any death regardless of cause. Drug levels (A) and coagulation 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 best long-term corrective and preventive action (CAPA) in this situation is a combination of user re-training, communication, and routine monitoring — as described in Option B .

According to the GCDMP (Chapter: Electronic Data Capture Systems) and FDA 21 CFR Part 11 , user credentials and electronic signatures in clinical systems are legally binding and must be used only by the assigned individual. Simultaneous log-ins under the same credentials often indicate credential sharing , a compliance violation that must be addressed through user education, reinforced security policies, and ongoing system oversight.

While technical controls (option A) may be considered, behavioral and procedural reinforcement are the first lines of defense. Options C and D are excessive and not aligned with proportional CAPA practices.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture (EDC) Systems, Section 7.1 – User Access, Authentication, and Training

FDA 21 CFR Part 11 – Electronic Records and Electronic Signatures, Sections 11.10(i) and 11.200(a)

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Access Control and Audit Trail Requirements

When using a copyrighted or validated rating scale (e.g., Hamilton Depression Scale, Visual Analog Pain Scale), any modification to the original instrument , including preprinting database codes on the CRF, must be approved by the instrument’s owner or licensing authority to ensure the validity and reliability of the instrument are not compromised.

According to the GCDMP (Chapter: CRF Design and Data Collection) , validated rating scales are psychometrically tested tools. Any visual or structural modification (such as adding codes, changing layout, or rewording questions) can invalidate prior validation results. Therefore, the CRF designer must consult the independent source (copyright holder) for approval and document that the validity of the tool remains intact .

Merely consulting statisticians (option B) or verifying database alignment (option D) does not ensure compliance. Thus, Option C ensures scientific and regulatory integrity.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 6.1 – Use of Validated Instruments and Rating Scales

ICH E6 (R2) GCP, Section 5.5.3 – Validation of Instruments and Data Capture Tools

FDA Guidance for Industry: Patient-Reported Outcome Measures – Use in Medical Product Development to Support Labeling Claims, Section 4 – Instrument Modification and Validation

When a company transitions from paper-based data capture to Electronic Data Capture (EDC) systems, one of the most critical areas requiring procedural updates is the Data Review and Validation SOP . The introduction of EDC systems fundamentally changes how data is collected, reviewed, validated, and queried.

According to the Good Clinical Data Management Practices (GCDMP) , the implementation of EDC introduces real-time data entry and review, automated edit checks, and electronic query management. These functionalities necessitate revised procedures to define how data validation, discrepancy management, and monitoring are conducted electronically. The SOP must specify roles, responsibilities, system access controls, and processes for electronic source verification (eSource), ensuring compliance with 21 CFR Part 11 and ICH E6 (R2) requirements.

Other SOPs such as Handling External Data or Data Backup may require minor updates, but the Data Review and Validation SOP undergoes the most extensive change because EDC technology shifts validation responsibilities from post-data entry review to real-time oversight within the system.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture (EDC) Systems, Section 6.3 – SOP Adaptation for EDC Implementation

FDA 21 CFR Part 11 – Electronic Records; Electronic Signatures

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Handling and Validation

In a relational database structure , each record in a table is uniquely identified by a primary key . In this case, the VITAL_SIGNS table uses a composite primary key consisting of:

Patient Identifier,

Visit Number, and

Vital Signs Parameter Name.

This means each record represents a unique measurement of a specific parameter (e.g., blood pressure, pulse) for a patient at a specific visit.

When joining PATIENT_DEMOGRAPHY and VITAL_SIGNS tables on the patient identifier , the result set will include one record for every combination of patient, visit, and parameter — i.e., one record per patient per visit per vital sign parameter .

Therefore, option C correctly describes the expected number of records.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 5.2 – Primary and Foreign Key Relationships in Relational Models

CDISC SDTM Implementation Guide, Section 5.3 – Observation-Level Data Structures

ICH E6(R2) GCP, Section 5.5.3 – Data Organization and Integration Principles

When coding adverse events (AEs) using MedDRA (Medical Dictionary for Regulatory Activities) , valid AE terms must correspond to specific, medically meaningful concepts that match directly to a Preferred Term (PT) or Lowest Level Term (LLT) in the dictionary.

Among the options, “Elevated HDL” (High-Density Lipoprotein) represents a single, medically interpretable, and standard term that can directly match to a MedDRA LLT or PT. This makes it suitable for auto-coding , where the system automatically maps verbatim terms to MedDRA entries without manual intervention.

In contrast:

ALT (B) and Abnormal SGOT (C) are incomplete or nonspecific; they describe test names or qualitative interpretations rather than events.

Option D lists multiple findings, making it too complex for automatic mapping. Such compound entries would require manual coding review .

According to GCDMP (Chapter: Medical Coding and Dictionaries) , a valid AE term should be:

Clinically interpretable (not just a lab test name)

Unambiguous

Single-concept based , not a collection of results

Thus, option A (Elevated HDL) is correct, as it aligns with MedDRA’s single-concept, standard terminology structure suitable for auto-coding.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Medical Coding and Dictionaries, Section 5.3 – Auto-coding and Verbatim Term Management

ICH M1 MedDRA Term Selection: Points to Consider, Section 2.1 – Coding Principles

ICH E2B(R3) – Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports

To monitor site variability in eligibility screening , you must analyze the number of subjects screened versus the number of subjects enrolled at each site. This allows identification of sites that are over- or under-screening relative to their enrollment yield.

The GCDMP (Chapter: Data Quality Assurance and Metrics) emphasizes that screening-to-enrollment ratios are critical indicators of protocol compliance and data quality. Sites with unusually low conversion rates may have unclear understanding of inclusion/exclusion criteria, requiring targeted training or monitoring.

Other options (A, C, D) provide enrollment metrics but do not reveal screening efficiency or variability , which depend on both screening and enrollment data .

Thus, option B correctly identifies the data necessary for monitoring eligibility screening performance across sites.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Metrics, Section 5.4 – Site Performance Metrics

ICH E6(R2) GCP, Section 5.18 – Monitoring and Site Oversight Requirements

In international or multicenter clinical studies, laboratory data often originate from different laboratories that use varying measurement units (e.g., mg/dL vs. mmol/L). The Good Clinical Data Management Practices (GCDMP, Chapter on CRF Design and Data Collection) provides clear guidance on managing this variability to ensure data consistency , traceability , and minimized transcription errors .

The approach that results in fewer transcription errors is to allow sites to enter lab values exactly as recorded in the source document (original lab report) and to require explicit selection of the corresponding unit from a predefined list on the data collection form or within the electronic data capture (EDC) system. This method (Option B) preserves the original source data integrity while enabling centralized or automated unit conversion later during data cleaning or statistical processing.

Option B also supports compliance with ICH E6 (R2) Good Clinical Practice (GCP) , which mandates that transcribed data must remain consistent with the source documents. Attempting to derive units automatically (Option A) can lead to logical errors, while forcing sites to manually convert units (Option D) introduces unnecessary complexity and increases the risk of miscalculation or inconsistent conversions. Printing only standard units on the CRF (Option C) ignores local lab practices and can lead to discrepancies between CRF entries and source records, triggering numerous data queries.

The GCDMP emphasizes that CRF design must account for local variations in measurement systems and ensure that unit selection is structured (dropdowns, controlled lists) rather than free-text to prevent typographical errors and facilitate standardization during data transformation.

Therefore, Option B —“Allow values to be entered as they are in the source and the selection of units on the data collection form”—is the most compliant, accurate, and efficient strategy for minimizing transcription errors in international lab data collection.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 5.4 – Laboratory Data Management and Unit Handling

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Data Handling and Record Retention

CDISC SDTM Implementation Guide, Section 6.3 – Handling of Laboratory Data and Standardized Units

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – Source Data and Accuracy of Data Entry

In a relational database system used in clinical data management, performance refers to how efficiently the system processes transactions, retrieves data, and handles large volumes of information without delay or data integrity issues. Among the listed options, loading a large lab data file into the database (Option B) has the most significant impact on database performance.

According to the Good Clinical Data Management Practices (GCDMP, Chapter on Database Design and Build) , the bulk data load process — such as importing large external datasets (e.g., central lab data, ECG results, or imaging metadata) — can be computationally intensive. This process engages the database’s input/output (I/O) subsystem, indexing mechanisms, and transaction logs simultaneously, often locking tables temporarily and consuming significant memory and processing resources.

Unlike standard CRF data entry (Option A) or record updates (Option D), which are incremental and typically processed in smaller transactional batches, bulk loading operations handle thousands or millions of rows at once. If not optimized (e.g., via staging tables, indexing strategies, or commit frequency control), such operations can degrade system performance, slow down concurrent user access, and increase the risk of transaction failure.

Executing a properly designed query (Option C) can also be resource-intensive depending on data volume and join complexity, but when queries are properly optimized (using indexed keys, efficient SQL joins, and selective retrieval), their impact is generally controlled and transient compared to large data imports.

Therefore, as outlined in the GCDMP Database Design and Build and FDA Computerized Systems Guidance , the most performance-impacting activity in a relational database is bulk loading large external datasets, making Option B the correct answer.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Build, Section 6.7 – Database Performance and Optimization

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – System Performance and Data Handling Efficiency

ICH E6 (R2) Good Clinical Practice, Section 5.5 – Data Handling and Record Integrity

CDISC Operational Data Model (ODM) Implementation Guide – Bulk Data Transfer and Validation Considerations

In studies utilizing Electronic Data Capture (EDC) systems, many traditional paper-based processes such as tracking and retrieving CRFs are eliminated or automated. However, query management and resolution remain essential because discrepancies, missing data, and protocol deviations still require clarification and correction, regardless of the data collection medium.

According to the GCDMP (Chapter: Data Validation and Cleaning) , data queries are 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.

A and D are obsolete with EDC (no physical CRFs).

B refers 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 C is 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

Prior to an audit, the auditee should expect to receive an audit plan or agenda , which outlines the scope, objectives, schedule, and logistics of the audit.

According to the GCDMP (Chapter: Quality Assurance and Audits) , an audit plan ensures transparency, preparation, and efficient execution. It typically includes details such as:

The audit scope and objectives,

The audit team members,

Documents or processes to be reviewed, and

The audit schedule and timeframe.

This allows the auditee to prepare the necessary records, staff, and facilities. While the auditor’s credentials (option A) may be shared informally, they are not a regulatory requirement. Corrective actions (option B) are outcomes of the audit, not pre-audit materials. Standard Operating Procedures (option C) may be requested during the audit but are not provided in advance.

Thus, Option D – Audit Plan or Agenda – is the correct and compliant answer.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Quality Assurance and Audits, Section 6.1 – Pre-Audit Planning and Communication

ICH E6 (R2) Good Clinical Practice, Section 5.19.3 – Audit Procedures and Responsibilities

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Section 8.1 – Audit Preparation and Planning

When laboratory data are missing from a paper-based clinical study, the Data Manager should direct data-entry personnel to issue a query to the investigative site for clarification or correction.

According to the Good Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning) , every missing, inconsistent, or out-of-range data point must be reviewed and, if necessary, resolved through the formal query management process . This ensures that all discrepancies between the source documents and database entries are properly documented, traceable, and auditable.

Data-entry staff are not authorized to infer or fill in missing information. They must escalate such discrepancies to the site via query, preserving data integrity and regulatory compliance with ICH E6 (R2) and FDA 21 CFR Part 11 . Calling the patient directly (option B) would violate confidentiality and site communication protocol, while simply flagging or ignoring the issue (options A and D) would not meet GCDMP query resolution standards.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 5.2 – Query Management and Resolution

ICH E6 (R2) Good Clinical Practice, Section 5.18.4 – Communication of Data Discrepancies

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