CCDM Certified Clinical Data Manager Question and Answers

TheScope of Work (SOW)is a project management document that defineswhat services are includedin the work agreement between the sponsor and the CRO or vendor. It outlines deliverables, responsibilities, timelines, and budget allocations.

According to theGCDMP (Chapter: Project Management in Data Management), the SOW includes specifications such as:

The number and frequency ofdata transfers,

Database build and lockmilestones,

Quality control deliverables, and

Resource allocationfor data management tasks.

The SOW does not cover operational site-level details such as monitoring schedules (B), protocol sampling details (C), or personnel contact lists (D).

Therefore,option A (number of data transfers budgeted for a project)correctly identifies a use case directly addressed in the SOW.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Project Management, Section 4.1 – Scope of Work and Resource Planning

ICH E6(R2) GCP, Section 5.5 – Sponsor Oversight and Data Management Responsibilities

PMI Project Management Framework – Scope Definition and Deliverable Specifications

The section of theClinical Study Report (CSR)most useful for aData Manageris thedescription of how data were processed.

According to theGCDMP (Chapter: Data Quality Assurance and Control), this section details thedata handling methodology— includingdata cleaning, coding, transformation, and derivation procedures— all of which are core responsibilities of data management. Reviewing this section ensures that the data processing methods documented in the CSR align with theData Management Plan (DMP),Data Validation Plan (DVP), anddatabase specifications.

Thestatistical methods section (option A)is primarily for biostatistics, and therationale for study design (option B)pertains to clinical and regulatory affairs.Clinical narratives (option D)are used by medical reviewers, not data managers.

By reviewing how data were processed, the Data Manager verifies that the study data lifecycle—from collection to analysis—was conducted in compliance with regulatory and GCDMP standards.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 6.3 – Documentation of Data Processing in Clinical Study Reports

ICH E3 – Structure and Content of Clinical Study Reports, Section 11.3 – Data Handling and Processing

FDA Guidance for Industry: Clinical Study Reports and Data Submission – Data Traceability and Handling Documentation

Thesite-work dimension of site performanceevaluates how efficiently sites manage and resolve data-related tasks — particularly query resolution, data entry, and correction timelines. Among the given metrics, themedian and range of time from query generation to resolution (D)directly measures the site’s responsiveness and data management efficiency.

According to theGCDMP (Chapter on Metrics and Performance Measurement), this indicator helps identify sites that delay query resolution, which can impact overall study timelines and data quality. Tracking this metric allows the data management team to proactively provide additional training or communication to underperforming sites.

Other options measure different aspects of project progress:

Areflects overall database closure speed.

BandCrelate to study startup and enrollment readiness, not ongoing data work.

Thus,option Daccurately represents asite performance timeliness metric, aligning with CCDM principles for operational performance measurement.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Metrics and Performance Management, Section 5.4 – Site Query Resolution Metrics

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

Case Report Form Completion Guidelines (CCGs)are essential study documents that instruct site staff on how to complete each field of the CRF correctly. Aminimum requirementfor CCGs, according toGood Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), is that they must includeversion control.

Version control ensures that all updates or revisions to the CCG—arising from protocol amendments or clarification of data entry rules—are documented, dated, and traceable. This guarantees that site personnel are always using the most current version and supports audit readiness.

Option A describes an important design consideration but not a minimum compliance requirement. Option B is inaccurate, as CCGs must be approved and implementedbefore data collection begins, not after. Option D includes an irrelevant stakeholder (Marketing).

Therefore,option C—“CCGs must include a version control on the updated document”—is correct and compliant with CCDM and GCP standards.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: CRF Design and Data Collection, Section 4.3 – Development and Maintenance of CRF Completion Guidelines

ICH E6(R2) GCP, Section 8.2.1 – Essential Documents and Version Control Requirements

Beforedatabase 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)isnot a data management function; it falls underclinical operationsandmonitoring responsibilities. While data management may review confirmation of site close-outs, the activity itself is not part of pre-database lock procedures.

Therefore,option Dcorrectly 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

Adding multiple new sites late in the enrollment period creates aconcentrated influx of new datanear 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 theGood Clinical Data Management Practices (GCDMP, Chapter: Project Management and Data Flow), late site activation compresses the timeline for data management tasks, necessitating increased resources fordata 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.

Whileoption D(increased query rates) can occur, it is a secondary effect. Themost direct and consistent impactis 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

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

To identifysite variability in eligibility screening, the most effective approach is toplot 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.

TheGCDMP (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 intoeligibility 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

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

The described process isBlind Verification, also known asdouble data entry with blind verification. In this method, two independent operators enter the same data. The second operator isblindedto the first entry to avoid bias. When discrepancies arise, the system flags them for review, and the second entry (or an adjudicated value) is retained as the correct one.

According toGCDMP (Chapter: Data Entry and Data Tracking), blind double data entry is used primarily inpaper-based studiesto minimize transcription errors and ensure data accuracy.

Single entry (D):Only one operator enters data.

Manual review (B):Involves post-entry checking, not during entry.

Third-party compare (C):Used for reconciling external data sources, not CRF data.

Hence,option A (Blind verification)is the correct and CCDM-defined process.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Entry and Data Tracking, Section 5.1 – Double Data Entry and Verification Methods

ICH E6(R2) GCP, Section 5.5.3 – Data Entry and Verification Controls

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.2 – Data Accuracy and Verification

The optimal method for collectingfrequent patient-reported pain datais throughelectronic Patient-Reported Outcomes (ePRO)with built-inreminder functionality.

According to theGCDMP (Chapter: Electronic Data Capture Systems), ePRO systems provide avalidated, real-time, and user-friendly interfacefor subjects to record time-sensitive data accurately. The use ofautomated remindersensures compliance with protocol-specified data collection times, improving data completeness and accuracy.

Paper diaries (option A) are prone torecall bias and backfilling, while daily site calls (option B) areresource-intensiveand introduce human error. IVRS systems (option C) are acceptable but less efficient and user-friendly than modern ePRO applications, which can integrate timestamp validation, compliance monitoring, and real-time alerts.

ePRO systems also comply withFDA 21 CFR Part 11andICH E6 (R2)for audit trails, authentication, and validation, making them the preferred solution for repeated PRO data collection.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture (EDC) Systems, Section 6.1 – Use of ePRO for Repeated Measures

FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 5 – ePRO Compliance and Validation

ICH E6 (R2) GCP, Section 5.5.3 – Electronic Data Systems and Recordkeeping

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

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

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

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

Reference (CCDM-Verified Sources):

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

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

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

Manual or visual data reviewis used to identifycomplex clinical relationships and contextual inconsistenciesthat cannot be detected by automated edit checks.

According to theGCDMP (Chapter: Data Validation and Cleaning), automated edit checks are ideal for structured validations, such as missing fields (option C), reference ranges (option D), or predefined value lists (option A). However, certain clinical cross-checks—such as verifyingadverse event treatments against concomitant medication records—requireclinical judgmentandcontextual understanding.

For example, if an adverse event of "severe headache" was reported but no analgesic appears in the concomitant medication log, the data may warrant manual review and query generation. These context-based checks are best performed by trained data reviewers or medical data managers during manual data review cycles.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.3 – Manual Review and Clinical Data Consistency Checks

ICH E6 (R2) Good Clinical Practice, Section 5.18.4 – Clinical Data Review Responsibilities

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Verification Principles

For a well-maintained 100-subject study withongoing data cleaningandcompleted reconciliations, thedatabase lockprocess typically takesa few daysafter theLast Patient Last Visit (LPLV).

According to theGCDMP (Chapter: Database Lock and Archiving), the duration of the lock process depends on the level of data cleanliness at LPLV. If the study team has conducted continuous data cleaning, query resolution, and external data reconciliation throughout the trial, then the final lock steps (e.g., final data review, documentation, and approvals) can be completed in2–5 days.

However, if significant cleaning or reconciliation remains outstanding, lock may take several weeks. Since the question states that data are “maintained almost all clean,”Option B – a few days– is the appropriate estimate.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Lock and Archiving, Section 6.2 – Database Lock Preparation and Timelines

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

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Lock and Archiving Procedures

Although clerical staff can technically perform data entry,data entry in clinical research requires study-specific training, particularly in the use of theElectronic Data Capture (EDC) systemand understandingdata discrepancy resolutionprocedures.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection)andICH E6 (R2), individuals responsible for data entry at clinical sites must bequalified by education, training, and experience. This includes understanding how to navigate the EDC system, enter data according to CRF Completion Guidelines, and appropriately respond to queries or system-generated edit checks.

Untrained clerical personnel may inadvertently introduce errors, violate Good Clinical Practice (GCP) standards, or fail to recognize protocol-relevant data. Therefore, theData Managermust ensure that site users receivestudy-specific and system trainingbefore gaining access to the EDC environment.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 5.2 – Investigator Site Training and Data Entry Requirements

ICH E6 (R2) Good Clinical Practice, Section 4.1.5 – Qualified Personnel and Training Requirements

FDA 21 CFR Part 11 – User Access and Training Provisions for Electronic Records

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

In aSQL (Structured Query Language)database, theSELECTstatement specifies which columns to display from a table. In this query, only three columns —Pt_ID,MRN, andSSN— are being selected from thepatienttable.

This means the resulting dataset will contain:

The same number ofrows (records)as the original table (assuming noWHEREfilter), and

Fewer columnsthan 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 isnarrower than the patient table, makingoption Dcorrect.

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

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

Even when allrangeandlogic checksare successfully resolved, discrepancies may still exist between theclinical databaseand thesource documents. This typically indicates anerror 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 theGood Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning),data validation rulessuch as range and logic checks are designed to identify inconsistencies, missing data, or out-of-range valueswithin the databaseitself. However, they donot verify the accuracy of data entry against the original source documents— that responsibility falls undersource 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 thatdata 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.

OptionC (Data were changed after checks were run)might occur in rare cases but would normally be documented in an audit trail per21 CFR Part 11andICH E6 (R2)standards. OptionBmisinterprets the issue, since “logical and in range” values can still be incorrect relative to the source. OptionA (Auditor error)is possible but statistically less likely, as source data verification follows strict, documented audit procedures.

Therefore, themost likely reasonfor such discrepancies isOption 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

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

To properly associatebody-composition data(from a DEXA scanner) with other study data, both thesubject numberand thevisit numberare required.

According to theGCDMP (Chapter: Data Management Planning and Study Start-up), every clinical data record must beuniquely identifiable and linkableto a specific subject and study event. Thesubject numberidentifies the participant, while thevisit numberdefines the temporal context in which the measurement was taken.

Without both identifiers, data integration becomes ambiguous—especially if multiple assessments occur over time (e.g., baseline, week 12, end of study). Including both ensuresdata traceability, integrity, and alignmentwith the protocol-defined schedule of events.

Study number (option A) alone does not distinguish between visits or subjects, and visit number alone (option C) lacks linkage to the individual participant.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 4.4 – Data Linking and Identification Requirements

ICH E6 (R2) GCP, Section 5.5.3 – Data Traceability Principles

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Identification Requirements

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

Duringdata reconciliationbetween a central laboratory and CRF data, the source of truth is typically thecentral lab database, as it provides directly measured, vendor-generated results.

When thecentral lab has data but the CRF does not (option C), the Data Manager must first query thecentral labto confirm that the result was transmitted correctly, since discrepancies may stem from data processing or timing issues. Once confirmed, a secondary query may be issued to the site to ensure CRF completion and alignment.

Conversely, if the CRF contains data but the central lab is missing results (option B), the issue is site-level, not vendor-level.

According to theGCDMP (Chapter: External Data Transfers and Reconciliation),priority for querying depends on the authoritative source— for lab data, thecentral labis considered the source of record.

Therefore,option Cis correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers and Reconciliation, Section 6.1 – Reconciliation of Central Lab and CRF Data

ICH E6(R2) GCP, Section 5.5.3 – Source Data Verification and Vendor Reconciliation

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.4 – Data Reconciliation and Traceability

Testing query rules withtest data inputs to confirm expected outputswithout examining the underlying program logic is an example ofblack box testing.

According to theGCDMP (Chapter: Data Validation and System Testing), black box testing is afunctional testing approachused to verify that the system performs correctly from the end-user’s perspective. In this method, testers input various conditions and observe outputs to ensure the system behaves as intended — for instance, that edit checks trigger correctly when data fall outside predefined limits.

In contrast,white box testinginvolves examining internal logic, code, and algorithm structures. Because data managers typically validate edit checks through data-driven test cases rather than code inspection,black box testingis the appropriate and industry-standard method. This ensures compliance with validation documentation standards as outlined inFDA 21 CFR Part 11, Section 11.10(a)andICH E6 (R2)system validation expectations.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Validation and Testing, Section 4.1 – Testing Approaches (Black Box and White Box)

FDA 21 CFR Part 11 – System Validation Requirements

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

Afront-end checkis a type ofreal-time validationperformed at the point of data entry—typically within anElectronic Data Capture (EDC)system or data entry interface—designed to ensure that the data entered in a text box (or any input field) isvalid, logically correct, and within expected parametersbefore the user can proceed or save the record.

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning),edit checksare essential components of data validation that ensure data accuracy, consistency, and completeness. Front-end checks are implemented within the data collection interface and are triggered immediately when data are entered. They prevent invalid entries (such as letters in numeric fields, out-of-range values, or improper date formats) from being accepted by the system.

Examples of front-end checks include:

Ensuring a numeric field accepts only numbers (e.g., weight cannot include text characters).

Validating that a date is within an allowable range (e.g., not before the subject’s date of birth).

Requiring mandatory fields to be completed before moving forward.

This differs fromback-end checksorprogrammed checks, which are typically run later in batch processes to identify data inconsistencies after entry.Manual checksare human-performed reviews, often for context or data that cannot be validated automatically (e.g., narrative assessments).

Front-end edit checks are preferred wherever possible because theyprevent errors at the source, reducing the number of downstream data queries and cleaning cycles. They contribute significantly todata quality assurance,regulatory compliance, andefficiency in data management operations.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.2 – Edit Checks and Real-Time Data Validation

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – Data Entry and Verification Controls

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

CDISC Operational Data Model (ODM) Specification – Edit Check Implementation Standards

Prior to an audit, theauditeeshould expect to receive anaudit plan or agenda, which outlines thescope, objectives, schedule, and logisticsof the audit.

According to theGCDMP (Chapter: Quality Assurance and Audits), anaudit planensures 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

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

The approach that results infewer transcription errorsis toallow sites to enter lab values exactly as recorded in the source document (original lab report)and to requireexplicit selection of the corresponding unitfrom 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 withICH 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.

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

Therefore, OptionB—“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

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

In a relational clinical database, themost efficient and normalized structurefor data collected repeatedly over time—such asvital signs—isone record per patient per visit.

Each patient will have multiple records, one for each visit when vital signs are assessed. This structure supports:

Time-based analysis (e.g., trends across visits),

Accurate data linkage with visit-level metadata, and

Efficient querying for longitudinal data.

According to theGCDMP (Chapter: Database Design and Build), the relational design principle dictates that data should be stored at thelowest unique level of observation. Since vital signs vary by both patient and visit, the combination ofpatient ID + visit IDforms a unique key for each record.

Option A (per visit) lacks patient identification, while options B and D aggregate data too broadly, losing temporal detail.

Thus,option C (One record per patient per visit)correctly represents the normalized design structure.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 4.2 – Normalization and Table Structure

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

ICH E6(R2) GCP, Section 5.5.3 – Data Handling Principles

TheQuery frequency counts per data element(Option D) is the best metric for identifyingdata trends and potential systemic data issuesin clinical trials.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Quality Assurance and Control),trend analysisinvolves identifying recurring data issues across subjects, sites, or variables to detect training gaps, protocol misinterpretation, or CRF design flaws. A high number of queries generated for specific fields (e.g., visit date, lab values, or dosing information) may indicate systemic problems such as unclear CRF instructions or site-level misunderstandings.

While metrics such aspercent of data cleaned (A)andtime to database lock (B)reflect overall progress and efficiency, they do not identifyspecific data pattern issues. Thenumber of subjects screened/enrolled (C)pertains to recruitment rather than data quality.

Therefore,query frequency per data elementprovides actionable insights for quality improvement, process refinement, and early identification of potential risks.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 6.3 – Metrics and Trend Analysis

ICH E6 (R2) Good Clinical Practice, Section 5.18.4 – Risk-Based Quality Review and Data Trends

FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section 6 – Data Metrics and Trend Evaluation

When an updated version of adata exchange standard(such as CDISC SDTM, ADaM, or ODM) is released, the first factor that should be assessed isbackwards compatibility. This determines whether the new version can interoperate with or accept data from prior versions without significant reconfiguration or data loss.

According to theGood Clinical Data Management Practices (GCDMP)andCDISC Implementation Guides, assessingbackwards compatibilityensures that historical or ongoing study data remain valid and usable within the updated environment. If the new version introduces structural or semantic changes (such as variable name modifications or controlled terminology updates), it could impact mapping, validation, or regulatory submissions.

Once backward compatibility is confirmed, secondary assessments such ascontent coverage,availability of overlapping standards, andmigration costcan be considered. However, ensuring that the new version supports existing infrastructure and data continuity is thefirst critical stepbefore adoption.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Standards and Data Integration, Section 4.2 – Data Standards Updates and Compatibility Considerations

CDISC SDTM Implementation Guide, Section 1.5 – Backward Compatibility and Version Control

ICH E6(R2) GCP, Section 5.5 – Data Handling and Standardization

In this scenario, the variable of interest—range of motion (ROM)—is aclinically measured, observer-dependent variable. The accuracy and reliability of such data depend primarily on theprecision and consistency of the measurement technique, not merely on data entry validation. Therefore, the most appropriatequality control (QC) methodisindependent verification of the measurement by a second qualified assessor during the visit(Option D).

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Quality Assurance and Control), quality control procedures must be tailored to the nature of the data. Forclinically assessed variables, especially those involving human judgment (e.g., physical measurements, imaging assessments, or subjective scoring),real-time verification by an independent qualified assessorensures that data are valid and reproducible at the point of collection. This approach directly addressesmeasurement bias,observer variability, andinstrument misuse, which are primary sources of data error in clinical outcome assessments.

Other options, while valuable, address onlydata consistency or plausibilityafter collection:

Option A (comparison to previous visit)andOption C (reviewing data listings)are retrospective data reviews, suitable for identifying trends but not preventing measurement error.

Option B (programmed edit checks)detects only extreme or impossible values, not measurement inaccuracies due to technique or observer inconsistency.

The GCDMP andICH E6 (R2) Good Clinical Practiceguidelines emphasize that data quality assurance should beginat the source, through standardized procedures, instrument calibration, and dual assessments for observer-dependent measures. Having anindependent second assessorensures inter-rater reliability and provides direct confirmation that the recorded value reflects an accurate and valid measurement.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 7.4 – Measurement Quality and Verification

ICH E6 (R2) Good Clinical Practice, Section 2.13 – Quality Systems and Data Integrity

FDA Guidance for Industry: Patient-Reported Outcome Measures and Clinical Outcome Assessment Data, Section 5.3 – Quality Control of Clinician-Assessed Data

SCDM GCDMP Chapter: Source Data Verification and Quality Oversight Procedures

Safety datacollected during a clinical trial are used primarily to supportproduct labeling, ensuring accurate communication of a drug’srisks, contraindications, and adverse reactionsto healthcare providers and patients.

According to theGCDMP (Chapter: Safety Data Handling and Reconciliation)andICH E2A/E2Fguidelines, alladverse events (AEs), serious adverse events (SAEs), and laboratory abnormalitiesare analyzed and summarized to define the safety profile of an investigational product. These data form the basis for regulatory submissions such as theClinical Study Report (CSR)andproduct labeling (e.g., prescribing information), as required by theFDAand other regulatory authorities.

While safety data may contribute indirectly to analyses such as survival curves (option A) or quality of life metrics (option D), theirprimary regulatory functionis to informproduct labelingand post-marketing surveillance documentation.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Safety Data Handling and Reconciliation, Section 4.3 – Use of Safety Data in Regulatory Submissions

ICH E2A – Clinical Safety Data Management: Definitions and Standards for Expedited Reporting

FDA Guidance for Industry: Adverse Event Reporting and Labeling Requirements

Database auditsare conducted to ensureongoing data accuracy, completeness, and compliancethroughout the lifecycle of a clinical trial. According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Quality Assurance and Control), quality audits are most effective when performedperiodically during study conduct, rather than waiting until study completion.

Performing audits periodically allows early detection of data entry errors, protocol deviations, and system inconsistencies, thereby reducing the risk of large-scale data issues before database lock. This proactive approach aligns withrisk-based quality management principlesoutlined inICH E6(R2)and ensures corrective actions are implemented in real time.

Options A and B represent reactive quality control, which occurs too late to prevent data issues. Option C (after first few cases) provides initial validation but does not ensure continuous oversight.

Therefore,option D — “Periodically throughout the study”— represents the optimal and compliant timing for quality audits of the database.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Control, Section 5.3 – Ongoing Quality Control and Auditing

ICH E6(R2) GCP, Section 5.1.1 – Quality Management System and Risk-Based Monitoring

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Data Review and Auditing Practices

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

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

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

This list typically includessubject identifiers,query types,dates generated, andstatus 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 Dis 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

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

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

If a blood sample is drawndistal (downstream)from a saline infusion site, it may becomecontaminated with saline, leading toabnormal laboratory results. Saline contains a high concentration of sodium chloride, which artificially elevates sodium while diluting other blood components.

Therefore, such samples would display:

Very high sodium levels, and

Abnormally low levelsof other analytes (e.g., proteins, glucose, potassium).

This abnormal pattern (option B) is a classic indicator ofsaline contamination.

Per theGCDMP (Chapter: Data Validation and Cleaning),cross-variable consistency checksare critical for identifying biologically implausible patterns, such as this one, which indicatepre-analytical errorsrather than true physiological changes.

Hence,option Baccurately describes the data signature of a contaminated blood draw.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 6.2 – Logical and Consistency Checks for Laboratory Data

ICH E6(R2) GCP, Section 5.1.1 – Data Quality and Biological Plausibility Checks

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – Detecting Laboratory Anomalies

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

The Monitoring Plan defines:

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

Theresponsibilities of monitors.

Thecommunication and escalation proceduresfor data quality and protocol compliance.

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

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

Reference (CCDM-Verified Sources):

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

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

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

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

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

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

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

Reference (CCDM-Verified Sources):

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

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

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