CCDM Certified Clinical Data Manager Questions and Answers

TheCase Report Form (CRF) Completion Guidelines (CCGs)are critical documents that guide site staff onhow to accurately and consistently record dataon CRFs across all participating sites, especially in multinational trials.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), one of the key components of the CCGs is alist of acceptable abbreviationsand conventions to be used during CRF entry. This standardization ensures data consistency across languages and countries, reduces ambiguity during data review, and facilitates database design and coding accuracy.

Whiletranslation (A)may be useful for training materials, it is not required for CCGs unless specified by regulatory bodies. OptionsCandDare incorrect because data collection should adhere to standardized terms in English (or the study’s official language) — allowing free use of local languages or arbitrary abbreviations introduces inconsistencies.

Hence,option B—“CCGs must contain the list of acceptable abbreviations to be used in the CRF”— is correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: CRF Design and Data Collection, Section 5.3 – CRF Completion Guidelines and Standardization

ICH E6(R2) GCP, Section 5.5.3 – Consistency and Data Recording Requirements

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.2 – Data Entry Conventions and Documentation

In initial vendor discussions forexternal data integration(e.g., central lab, ECG, imaging vendors), themost critical and foundational topicis defining theacceptable record, field, and file formats.

According to theGCDMP (Chapter: External Data Transfers and Integration), establishing theData Transfer Specifications (DTS)early in the process ensures consistent structure, proper mapping, and compatibility between the vendor’s system and the sponsor’s database. These specifications define:

Data structure (variable names, formats, delimiters)

File naming conventions

Frequency of transfers

Methods of secure data transmission

Discussing formats first allows later alignment on data validation, quality metrics, and dictionary standards (which occur in subsequent stages). Without format agreement, all downstream processes risk misalignment, resulting in data incompatibility and rework.

Thus,option C (Acceptable record, field, and file formats)correctly represents the foundational focus of initial vendor discussions for ensuring data quality and integration efficiency.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers and Integration, Section 4.1 – Data Transfer Planning and Specification Development

ICH E6(R2) GCP, Section 5.5.3 – Data Handling and System Validation

FDA Guidance: Computerized Systems Used in Clinical Investigations, Section 6.3 – Data Import and Format Control

The primary purpose of maintaining anaudit trailas required under21 CFR Part 11isto preserve data integrity. According to the U.S. FDA’s regulation on electronic records and signatures, every change to electronic data must be traceable, including information about who made the change, when it was made, and what the change entailed.

TheGood Clinical Data Management Practices (GCDMP)outlines that an audit trail provides a permanent, chronological record of all modifications to clinical data. This ensures transparency and allows the reconstruction of the course of data entry and modification. The regulation aims to prevent unauthorized or undocumented data manipulation, thereby maintainingthe accuracy, reliability, and validityof electronic records.

TheFDA 21 CFR Part 11, Section 11.10(e)explicitly mandates that systems must usesecure, computer-generated, time-stamped audit trailsto independently record the date and time of operator entries and actions that create, modify, or delete electronic records. This ensures the data remains trustworthy and defensible in regulatory reviews or inspections.

Therefore, the main reason for requiring an audit trail isto preserve data integrity— ensuring that all data captured, modified, or transmitted is authentic, accurate, and complete throughout the study lifecycle.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Regulatory Compliance and Data Integrity

FDA 21 CFR Part 11 – Electronic Records; Electronic Signatures, Section 11.10(e)

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

This event qualifies as aSerious Adverse Event (SAE)because itresulted in a prolonged hospitalization, even though the episode itself was mild.

According toICH E2AandGCDMP (Chapter: Safety Data Handling and Reconciliation), an adverse event is considered“serious”if it results in any of the following outcomes:

Death,

Life-threatening situation,

Hospitalization or prolongation of existing hospitalization,

Persistent or significant disability/incapacity, or

Congenital anomaly/birth defect.

The severity (mild, moderate, severe) describesintensity, while seriousness describesregulatory significance and medical outcome. Thus, a mild tachycardia episode leading to extended hospital stay meets theregulatory definition of an SAE.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Safety Data Handling and Reconciliation, Section 5.2 – Definition and Classification of Serious Adverse Events

ICH E2A – Clinical Safety Data Management: Definitions and Standards for Expedited Reporting, Section II – Seriousness Criteria

FDA 21 CFR 312.32 – IND Safety Reporting: Serious Adverse Event Definitions

In anophthalmology clinical study, data criticality is determined by how directly a data element affectssafety evaluation,efficacy assessment, andregulatory decision-making. According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning), critical data fields are those that:

Have a direct impact on theprimary and secondary endpoints, or

Are essential forsafety interpretation and adverse event causality assessment.

Among the listed options,Concomitant Medications (Option B)are consideredcritical datafor ophthalmology studies. This is because many ocular treatments and investigational products can interact with systemic or topical medications, potentially affectingocular response,intraocular pressure,corneal healing, orvisual function outcomes. Any inconsistency in concomitant medication data could directly influencesafety conclusionsorefficacy 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.

PerGCDMPandICH E6 (R2) GCPguidelines, data validation plans must definecritical data fieldsduring study setup, reflecting therapeutic area–specific priorities. For ophthalmology,concomitant medications, ocular assessments (visual acuity, intraocular pressure, retinal thickness, etc.), and adverse eventsare 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, theConcomitant 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)

Inpaper-based clinical studies,front-end data checks(those performed during data entry) are intentionally kept minimal to ensure thatdata are entered exactly as recorded on the paper CRF. This principle ensuresdata integrityby maintaining fidelity between source and electronic records before any cleaning or edit validation occurs.

TheGCDMP (Chapter: Data Validation and Cleaning)explains that data entry operators should input values as written, even if they appear incorrect or inconsistent, because the purpose of front-end checks is not to interpret but to capture data faithfully. Theback-end edit checks—performed later by data managers—are designed to identify inconsistencies, out-of-range values, or logical errors that require clarification through queries.

This approach separatesdata capturefromdata cleaning, minimizing bias and preserving original investigator input. Hence,option Aaccurately states the rationale for keeping front-end checks minimal in paper-based studies.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 4.2 – Data Entry, Edit Checks, and Query Process

ICH E6(R2) GCP, Section 5.5.3 – Data Handling and System Controls

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.1 – Data Entry and Verification Processes

When an investigator retires mid-study and the replacement refuses to use theElectronic Data Capture (EDC)system, thedata managermust not take unilateral action but rathercollaborate with the study teamto explore acceptable solutions.

Per theGCDMP (Chapter: Project Management in Data Management), any deviation from the established data capture method — particularly a change that affects regulatory compliance, data consistency, or site operations — requires a cross-functional assessment. The study team, which includes clinical operations, project management, regulatory affairs, and data management, should evaluate feasible alternatives such as:

Allowing paper CRF entry followed by centralized data transcription,

Retraining site staff on EDC use, or

Temporarily suspending data entry until compliance can be restored.

Immediate site closure (option A) or unilateral decisions by data management (options C and D) violate escalation and communication protocols. Collaborative decision-making ensures continuity, compliance, and data integrity, in line withICH E6 (R2) GCPandFDA 21 CFR Part 11.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Project Management and Communication, Section 5.2 – Handling Site and Investigator Changes

ICH E6 (R2) Good Clinical Practice, Section 4.1 – Investigator Responsibilities

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Section on EDC Operations and Site Management

Whenmerging data from different clinical trials, theuse of a common adverse event (AE) dictionary(such asMedDRAorWHO Drug) is essential to ensure consistency and comparability across datasets.

According to theGCDMP (Chapter: Standards and Data Mapping)andCDISC SDTM Implementation Guide, data integration across studies requires standardized terminology for adverse events, medications, and clinical outcomes. Using the same AE dictionary ensures that similar terms are coded consistently, allowing accurate cross-study analysis, pooled summaries, and safety reporting.

A sharedsoftware platform (option A)is not necessary if data are mapped to standard formats (e.g., CDISC SDTM). Patient population similarity (option B) affects interpretation but not technical data merging. Study design differences (option C) may influence statistical analysis but not data integration mechanics.

Therefore,Option D – Use of a common adverse event dictionary– is the correct and most critical action for consistent multi-study data integration.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Standards and Data Mapping, Section 5.1 – Use of Standardized Coding Dictionaries

CDISC SDTM Implementation Guide, Section 4.3 – Controlled Terminology and Cross-Study Integration

ICH E3 and E2B – Clinical Data Standards and Safety Coding Requirements

InElectronic Data Capture (EDC)systems, edit checks are categorized based on when and how they are executed — typicallyimmediate (at data entry)orbatch (post-entry). Checks that require data frommultiple visits or formsare generallyinefficient to run at data entrybecause they depend on information that may not yet exist in the system.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning),cross-visit consistency checks— such as comparing baseline and follow-up blood pressure or verifying date order between screening and dosing — should be executed asbatch or scheduled validations, not at the point of data entry. Running these complex checks in real time can slow system performance, increase query load unnecessarily, and confuse site users if related data are not yet entered.

Conversely, edit checks against valid ranges, formats, or predefined value lists (options A, C, and D) are simple, local validations ideally performed immediately at data entry to prevent basic errors.

Therefore,cross-visit consistency checks(Option B) are best executed later, making theminefficient for real-time data entry validation.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.4 – Real-Time vs. Batch Edit Checks

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Section on Edit Checks and Data Validation Logic

CDISC SDTM Implementation Guide – Section on Temporal Data Consistency Validation

The section of theClinical Study Report (CSR)that is most useful for a Data Manager is the one that includes theenumeration and explanation of data errors. This section provides a summary of thedata quality control findings, including error rates, missing data summaries, and any issues identified during data review, validation, or database lock.

According to theGCDMP (Chapter: Data Quality Assurance and Control), post-study reviews of data errors and quality findings are essential for evaluating process performance, identifying recurring issues, and informing continuous improvement in future studies.

Other sections, such as clinical narratives (A) or statistical methods (C), are outside the core scope of data management responsibilities. Thedata error enumeration sectiondirectly reflects the quality and integrity of the data management process and is therefore the most relevant for review.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Control, Section 6.4 – Quality Reporting and Error Analysis

ICH E3 – Structure and Content of Clinical Study Reports, Section 14.3 – Data Quality Evaluation

The primary reason astatistician reviews the Case Report Form (CRF)is to ensure thatthe data being collected will support the planned statistical analysesfor bothsafety and efficacy endpoints.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), CRF design should always align with thestatistical analysis plan (SAP)to ensure that all necessary data elements are collected accurately and in analyzable formats. The statistician verifies that the CRF captures:

All endpoints specified in the protocol

Proper derivation or calculation fields

Timing of assessments

Consistency across visits and forms

Options B, C, and D address secondary or technical design considerations but not theprimary analytical purpose. The review ensures that the CRF provides a complete and analyzable dataset for meeting study objectives, regulatory submissions, and statistical integrity.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: CRF Design and Data Collection, Section 4.4 – Role of Statistics in CRF Design

ICH E9 – Statistical Principles for Clinical Trials, Section 5.2 – Data Collection and Analysis Alignment

FDA Guidance for Industry: E6(R2) GCP, Section 5.1 – Quality Management and Design Input from Stakeholders

During amajor system upgrade, it is critical to verify thatarchived data remain accessible, readable, and intactfollowing the implementation.

According to theGCDMP (Chapter: Database Lock and Archiving), regulatory requirements such as21 CFR Part 11andICH E6(R2)mandate that archived data must remain retrievable in ahuman-readable formatfor the duration of retention (often years after study completion).

Therefore, as part ofvalidation and verification testing, organizations must confirm that existing archives can still be accessed using the upgraded system or compatible tools.

Option A:Updating archive formats could alter original data integrity (noncompliant).

Option C:Migration offsite is an IT infrastructure task, not directly tied to the upgrade process.

Option D:Comparing CRFs to archives is unnecessary unless data corruption is suspected.

Hence,option B (testing archive accessibility)is the correct and compliant approach.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.4 – System Upgrades and Archive Validation

ICH E6(R2) GCP, Section 5.5.3 – System Validation and Data Retention

FDA 21 CFR Part 11 – Data Archiving, Retention, and Retrieval Requirements

The best choice for managingStandard Operating Procedures (SOPs)in a compliant and auditable manner is aDocument Management System (DMS).

According to theGCDMP (Chapter: Regulatory Requirements and Compliance)andICH E6 (R2), SOPs must beversion-controlled, securely stored, retrievable, and auditable. Avalidated DMSsupports controlled access, document lifecycle management (draft, review, approval, and archival), and electronic audit trails, ensuring full compliance withFDA 21 CFR Part 11andGood Documentation Practices (GDP).

WhileLearning Management Systems (C)track training, they are not intended for document control.Spreadsheets (B)andpaper systems (D)cannot provide adequate version tracking, access security, or audit capability required for regulatory inspection readiness.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Regulatory Requirements and Compliance, Section 5.2 – SOP Management and Document Control

ICH E6 (R2) GCP, Section 5.5.3 – Document and Record Management

FDA 21 CFR Part 11 – Electronic Records and Signatures, Section 11.10 – System Validation and Document Controls

A completedata element definitionin clinical data management should include:

Nameandclear descriptionof the data element,

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

Precisionor scale (if numeric), and

Unit or dimensionalityof measure (e.g., centimeters, inches).

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

TheGCDMP (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

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

When existingSDTM (Study Data Tabulation Model)domains do not cover specific efficacy data, thebest practiceis to firstsearch for relevant data element standardsthat may be available throughCDISC CDASH (Clinical Data Acquisition Standards Harmonization)or other recognized industry standards.

PerGCDMP (Chapter: Standards and Data Integration), Data Managers must ensure that new CRF elements are consistent withstandardized definitions, controlled terminology, and data models to support interoperability, future analysis, and regulatory submission.

If no existing standards exist, only then should the Data Manager collaborate with the study team to define new elements — but standard searches always come first.

Thus,option Cis correct —search for relevant data element standardsensures alignment with CDISC best practices and regulatory expectations.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Standards and Data Integration, Section 5.1 – Use of CDISC Standards in CRF Design

CDISC CDASH Implementation Guide, Section 4.1 – Standardization of Data Collection Fields

FDA Study Data Technical Conformance Guide (SDTCG), Section 2.4 – Use of Standard and Custom Domains

Standard Operating Procedures (SOPs)are formal, controlled documents that definestandardized processesto ensure clinical trials are conducted in compliance withGood Clinical Practice (GCP), the study protocol, and regulatory requirements (such as ICH and FDA).

According toGood Clinical Data Management Practices (GCDMP)andICH E6(R2) GCP, SOPs are fundamental to quality management systems. They describehowtasks are performed, ensuring consistency, accountability, and traceability across all studies and team members. Proper adherence to SOPs guarantees that data areaccurately generated, documented, and reportedin compliance with ethical and regulatory standards.

Other options serve different purposes:

SAP (B)defines statistical methodology, not compliance control.

DMP (C)focuses on study-specific data handling, not organizational compliance.

CRFs (D)are tools for data collection but do not enforce compliance by themselves.

Therefore,option A (SOP)is correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Quality Management and Compliance, Section 5.1 – Role of SOPs in Regulatory Compliance

ICH E6(R2) GCP, Section 2.13 and 5.1.1 – Quality Management Systems and SOP Requirements

FDA 21 CFR Part 312.50 – Sponsor Responsibilities and Compliance Systems

Assuming the data transfer, integration, and validation processes are properly controlled and compliant,electronic acquisitionof clinical data from medical devices is preferred because it allowsmore frequent and accurate data collection, leading to higher data resolution and integrity.

Per theGCDMP (Chapter: Technology and Data Integration), automated data collection minimizes manual transcription and reduces latency in data capture, ensuring both efficiency and completeness. While manual processes introduce human transcription errors and limit frequency, continuous electronic data capture can record thousands of accurate, time-stamped measurements, improving the study’s analytical power.

However,option Dslightly overstates the case — human error isreduced, not entirely eliminated, since setup, calibration, and integration still involve human oversight. Therefore,option Cis the best and most precise response, emphasizing the advantage of more robust and complete data capture.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Technology and Data Integration, Section 5.4 – Automated Data Acquisition and Validation

ICH E6(R2) GCP, Section 5.5.3 – Validation of Computerized Systems and Electronic Data Sources

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – Direct Data Capture from Instruments and Devices

Enrolling subjects before theElectronic Data Capture (EDC) systemis ready poses majordata integrity and compliance risks. The primary issue is thatdata cannot be accurately captured, validated, or monitoredwithout the system in place.

Per theGCDMP (Chapter: Data Management Planning and Study Start-up), data collection systems must befully validated, tested, and releasedbefore 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 aresecondary effects. The fundamental consideration is data capture capability and monitoring control, makingoption Acorrect.

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

When a clinical study usesblood pressure (BP)as anefficacy endpoint, the most reliable and standardized method of data collection is throughstudy-provisioned equipment.

According to theGCDMP (Chapter: CRF Design and Data Collection), data collected for primary efficacy endpoints must beconsistent, accurate, and standardized across all investigative sites. Usingstudy-provided calibrated equipmentensures that measurements are taken under uniform conditions, eliminating inter-site variability due to differences in devices, calibration, or measurement methods.

Collecting BP data from medical records (option A) risks inconsistent timing and techniques. Using each site’s own equipment (option B) introduces variability, while patient self-reports (option D) lack reliability and objectivity.

Thus, thebest practiceis to provision and standardize all equipment used to collect endpoint-related physiological data, ensuring regulatory-quality results suitable for analysis.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 5.1 – Standardization of Clinical Measurements

ICH E6 (R2) GCP, Section 5.5.3 – Data Accuracy and Equipment Standardization

FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 4.3 – Data Capture and Standardization Requirements

During a regulatory inspection, inspectors expect to find documentedStandard Operating Procedures (SOPs)governing the use, validation, and maintenance ofcomputerized systems, includingdata backup and recovery procedures.

According to theGCDMP (Chapter: Computerized Systems and Compliance)andFDA 21 CFR Part 11, organizations must maintain an SOP that ensuresdata protection against loss, corruption, or unauthorized access. The SOP should describe backup frequency, secure storage, verification of backup integrity, and procedures for data restoration.

While theData Management Plan (A)andEdit Specifications (D)are study-level documents, and theStatistical Analysis Plan (C)focuses on analysis procedures,only a Data Backup Plan (B)constitutes a requiredsystem-level SOPensuring compliance and data continuity.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Computerized Systems and Compliance, Section 5.2 – Data Security, Backup, and Recovery SOPs

FDA 21 CFR Part 11 – Subpart B, Controls for Closed Systems

ICH E6(R2) GCP, Section 5.5.3 – System Security, Data Backup, and Recovery Requirements

When aCRF modificationleads to a sudden increase inEDC queries, the most likely cause is an error or misconfiguration in theedit checksintroduced during or after the change. Therefore, the first step should be toreview the edit checks for correctness.

TheGCDMP (Chapter: Database Design and Validation)emphasizes that any database or CRF modification should triggerretesting 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 isOption C — review and validate the edit checksto 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

In arelational database structure, each record in a table is uniquely identified by aprimary key. In this case, theVITAL_SIGNStable uses acomposite primary keyconsisting of:

Patient Identifier,

Visit Number, and

Vital Signs Parameter Name.

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

When joiningPATIENT_DEMOGRAPHYandVITAL_SIGNStables on thepatient identifier, the result set will includeone record for every combination of patient, visit, and parameter— i.e.,one record per patient per visit per vital sign parameter.

Therefore,option Ccorrectly 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

Thekey deliverables for User Acceptance Testing (UAT)are theTest Plan, Test Scripts, and Test Results.

According to theGCDMP (Chapter: Database Design and Validation), UAT is the final validation step before a clinical database is released for production. It confirms that the system performs according to user requirements and protocol specifications.

The deliverables include:

UAT Test Plan:Defines testing objectives, scope, acceptance criteria, and responsibilities.

UAT Test Scripts:Provide step-by-step instructions for testing database functionality, edit checks, and workflows.

UAT Test Results:Document actual test outcomes versus expected outcomes, including any deviations and their resolutions.

These deliverables form part of the system validation documentation required underFDA 21 CFR Part 11andICH E6 (R2)to demonstrate that the database has been properly validated.

Project Plans (option A) and Training (option B) occur in earlier phases, while eCRF Completion Guidelines (option D) support site data entry, not system validation.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Validation, Section 5.3 – User Acceptance Testing Deliverables

FDA 21 CFR Part 11 – Validation Documentation Requirements

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – System Validation Records

When reporting milestone-based site payment information, themost critical informationto include isexpected versus actual milestones met to date, by site.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Project Management and Communication), effective reporting must supportoperational and financial decision-makingby presenting performance indicators in a clear, actionable format. Site payments in clinical studies are typically tied to specific milestones such as subject enrollment, visit completion, or data cleaning achievements.

By comparingexpected (planned)versusactual (achieved)milestones per site, the Data Manager providesclinical operations staffwith an accurate view of site progress and payment eligibility. This allows for identification of delayed sites, forecasting of upcoming payments, and early intervention for underperforming centers.

While milestone summaries by month or type (options A and B) may be useful for trend analysis, they lack the operational detail required for financial tracking. Milestone data by patient (option D) is overly granular for site-level payment management.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Project Management and Communication, Section 6.2 – Data Reporting for Site Performance and Payments

ICH E6 (R2) Good Clinical Practice, Section 5.18.4 – Communication and Monitoring Reports

FDA Guidance for Industry: Oversight of Clinical Investigations – Site Management and Reporting

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

When efficacy data arenot covered by existing CDISC SDTM domains, thefirst resourcethe Data Manager should consult is theCDISC Therapeutic Area Implementation Guide (TAIG)for that therapeutic field.

According to theGCDMP (Chapter: Standards and Data Mapping), CDISC’s Therapeutic Area User Guides (TAUGs) and Implementation Guides providestandardized data structures, variable definitions, controlled terminology, and implementation examplesfor specific diseases or therapeutic areas. These guides ensure consistency across studies, promote interoperability, and align data collection with regulatory submission expectations.

Consulting other sponsors’ forms or external registries (options A and C) can be informative but do not provide authoritative CDISC-compliant standards. SNOMED terms (option B) address medical terminology, not structural data domain definitions.

Therefore,Option Dis correct—CDISC TA Implementation Guidesare the recognized primary reference when extending or designing SDTM-compliant CRFs.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Standards and Data Mapping, Section 4.2 – Use of CDISC Standards

CDISC Therapeutic Area User Guides (TAUGs) – Implementation Guidance for Domain Extension

FDA Data Standards Catalog – CDISC Therapeutic Area Standards

Inpaper-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, inEDC-based studies, thesite coordinatoris directly responsible forentering data into the EDC system. This eliminates the need for centralized double data entry and shortens data cleaning timelines.

TheGCDMP (Chapter: Electronic Data Capture Systems)states that EDC systems shift certain tasks, includingdata entry, initial query response, and source verification preparation, to the site level. Yet,data entryremains 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

To submit study data to theFDA in CDISC SDTM format, the sponsor mustmap and transformthe collected data from the study’s operational database (e.g., EDC) intoSDTM-compliant domains.

According toGCDMP (Chapter: Standards and Data Integration)andCDISC SDTM Implementation Guide, this process includes:

Mappingraw data elements from the clinical database to SDTM domains (e.g., DM, AE, VS).

Transformingdata to comply with SDTM structural and naming conventions.

Validatingthe output using CDISC compliance tools (e.g., Pinnacle 21).

Re-entering data (B) is unnecessary, and a letter of intent (C) is not required. SDTM is explicitly accepted by FDA for both retrospective and prospective submissions, so (D) is incorrect.

Thus,option Ais correct —map and transform existing data to SDTM formatfor regulatory submission.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Standards and Data Integration, Section 5.3 – Data Transformation and CDISC Mapping

CDISC SDTM Implementation Guide, Version 3.4 – Data Conversion and Submission Requirements

FDA Study Data Technical Conformance Guide, Section 2.2 – SDTM Mapping and Validation

When adata inconsistencyarises — such as a record of “worsening of migraine” without prior documentation of a migraine episode — the Data Manager shouldquery the site for clarification(Option D).

According to theGCDMP (Chapter: Data Validation and Cleaning), data managers must raise aclarification querywhenever data appear incomplete, inconsistent, or ambiguous. The site must confirm whether “worsening of migraine” refers to anew eventor anexacerbation of a preexisting condition. This clarification ensures accurate safety reporting and appropriate medical coding (e.g., MedDRA classification).

Checking the medical history (Option C) may help but does not resolve the inconsistency. Assuming a relationship (Option A or B) without verification would violateGood Clinical Data Management Practiceand potentially misrepresent the adverse event.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.3 – Query Generation and Resolution

ICH E2A – Clinical Safety Data Management: Definitions and Standards for Expedited Reporting, Section II – Data Clarification Requirements

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

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

TheMedDRA (Medical Dictionary for Regulatory Activities)is a standardized medical terminology used for coding and analyzing adverse event (AE) and medical history data in clinical trials. Its hierarchical structure supports aggregation, analysis, and reporting across varying levels of medical specificity.

Frommost specific to least specific, the hierarchy is as follows:

Lowest Level Term (LLT):The most granular term, often reflecting the verbatim text reported by the investigator.

Preferred Term (PT):The standardized medical concept representing one or more LLTs describing the same condition.

High Level Term (HLT):A grouping of related PTs describing similar medical conditions.

High Level Group Term (HLGT):A broader grouping of related HLTs.

System Organ Class (SOC):The highest level of classification, grouping HLGTs by body system or etiology (e.g., cardiac disorders, infections).

Thus, the correct order — frommost specific to least specific— is:

LLT → PT → HLT → HLGT → SOC, which corresponds tooption D.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Medical Coding and Dictionaries, Section 5.2 – MedDRA Hierarchical Structure

ICH M1 MedDRA Terminology Guide, Version 26.0 – Hierarchy Overview

ICH E2B(R3) Guidelines – Clinical Safety Data Management

TheInformed Consent Form (ICF)is the document that explicitly describes what study subjects can expect regardingdata disclosure, privacy, and confidentialityduring and after participation in a clinical trial. According toICH E6 (R2) Good Clinical PracticeandFDA 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.

TheGCDMPreiterates that clinical data managers must ensure that all data handling practices align with the privacy commitments made in the ICF. This includes compliance withdata protection regulationssuch 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 theprotocolordata sharing planmay outline procedures and responsibilities but do not directly inform participants of their rights and data use expectations. Only theICFis 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

Providing the central laboratory vendor with acomplete subject demographic listingallowsongoing reconciliationbetween the sponsor’s EDC system and the vendor’s laboratory databaseduring study conduct.

TheGCDMP (Chapter: External Data Transfers and Integration)emphasizes thatsubject reconciliationensures that all laboratory data correspond to valid enrolled subjects and visits. Regular reconciliation throughout the study prevents data mismatches, missing results, or misassigned lab reports.

This proactive measure supports timely query resolution and data integrity across systems. Waiting until after database lock (as in option A) would delay corrections and risk inconsistencies. Options B and D address secondary benefits but not theprimary purpose—ongoing subject-level reconciliation.

Thus,option Cis correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers, Section 4.4 – Reconciliation and Vendor Communication

ICH E6(R2) GCP, Section 5.5.3 – Data Management, Reconciliation, and Integration

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – External Data Management

Whenlaboratory dataare missing from a paper-based clinical study, theData Managershould directdata-entry personnel to issue a queryto the investigative site for clarification or correction.

According to theGood 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 formalquery management process. This ensures that all discrepancies between the source documents and database entries are properly documented, traceable, and auditable.

Data-entry staff arenot authorizedto infer or fill in missing information. They must escalate such discrepancies to the site via query, preservingdata integrityandregulatory compliancewithICH E6 (R2)andFDA 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

When integratingexternal environmental datasuch asair quality readingswith clinical study data, it is essential to uselocation and time identifiersto properly align the environmental data with subject-level data.

According to theGood 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 usingcommon linkage variablesthat 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 poseprivacy and data protection risks. Instead, linkage is typically done at theaggregate (site or regional) level, maintaining compliance withHIPAAandGDPR.

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

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

According to theGood 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 with21 CFR Part 11andICH E6 (R2)requirements.

Other SOPs such asHandling External DataorData Backupmay require minor updates, but theData Review and Validation SOPundergoes 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 studies where themedical record serves as the source document, theElectronic Data Capture (EDC)system users (typically study coordinators or site personnel) must have appropriatetraining on how to access and log into the medical record system. This competency ensures that data abstracted from the electronic medical record (EMR) are complete, accurate, and verifiable in compliance with Good Clinical Practice (GCP) andGood Clinical Data Management Practices (GCDMP).

According to theGCDMP (Chapter: EDC Systems and Data Capture)andICH E6(R2), all personnel involved in data entry and verification must be trained in both the EDC and the primary source systems (e.g., EMR). This ensures that the integrity of data flow—from source to EDC—is maintained, and that personnel understand system access controls, audit trails, and proper documentation of source verification.

Whileresolving discrepant data (C)andscreening subjects (A)are part of study operations, thecompetency directly related to EDC system use in EMR-based studiesis the ability to properly log into and navigate the medical records system to extract source data.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Electronic Data Capture (EDC), Section 5.1 – Source Data and System Access Requirements

ICH E6(R2) Good Clinical Practice, Section 4.9 – Source Documents and Data Handling

FDA Guidance: Use of Electronic Health Record Data in Clinical Investigations, Section 3 – Investigator Responsibilities

When using acopyrighted or validated rating scale(e.g., Hamilton Depression Scale, Visual Analog Pain Scale), anymodification to the original instrument, including preprinting database codes on the CRF, must beapproved by the instrument’s owner or licensing authorityto ensure thevalidity and reliabilityof the instrument are not compromised.

According to theGCDMP (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 mustconsult the independent source (copyright holder)for approval anddocument 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 Censures 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

TheInvestigatorholds theprimary responsibilityfor ensuring the accuracy, completeness, and timeliness of Case Report Form (CRF) entries. This responsibility is mandated by regulatory requirements underICH E6(R2) Good Clinical Practice (GCP).

The investigator may delegate CRF completion to aqualified 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.

TheGCDMP (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

When electronic patient-reported outcomes (ePRO) devices are used, data are captured directly by subjects through validated devices and transmitted electronically to the study database. To ensurereal-time data quality control,programmed edit checksshould be implementedat the point of data entry— that is, as subjects input data into the device.

According toGood Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning),front-end programmed edit checksare the optimal method to prevent entry of invalid or out-of-range values in ePRO systems. This helps maintain data accuracy at the source, minimizing downstream queries and data cleaning workload.

OptionsAandBinvolve post-submission or manual review, which is less efficient and not compliant with the principle offirst-pass data validation.Option C(visualization) is a valuable secondary QC method for trends, but not for immediate data validation.

Therefore,option Dis correct —programmed edit checks upon data entryensure immediate validation and higher data integrity.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 5.3 – Automated Edit Checks and Front-End Validation

ICH E6(R2) GCP, Section 5.5.3 – Computerized System Controls and Validation

FDA Guidance for Industry: Electronic Source Data in Clinical Investigations (2013), Section 6 – Real-Time Data Quality Control

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