The most effective method for quality control (QC) of the query resolution process is to perform 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 the GCDMP (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 but do not assess quality . True quality control focuses on ensuring that data corrections preserve accuracy, auditability, and traceability — the fundamental principles of data integrity in 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

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

Name and clear description of the data element,

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

Precision or scale (if numeric), and

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

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

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

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

Reference (CCDM-Verified Sources):

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

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

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

In a CRF-to-database quality control (QC) audit , auditors compare data recorded on the paper Case Report Form (CRF) with data entered in the electronic database. If discrepancies exist that cannot be explained by documented data handling conventions , they are classified as audit findings .

Per GCDMP (Chapter: Data Quality Assurance and Control) , data handling conventions define acceptable data entry practices, transcription rules, and allowable transformations. These conventions ensure that CRF data are consistently interpreted and entered.

If a discrepancy deviates from these established rules, it indicates a process gap or error in data entry, validation, or training. Discrepancies justified by protocol design or CRF guidelines would not constitute findings.

Therefore, option C (Discrepancy not explained by the data handling conventions) correctly identifies the criterion for a true QC audit finding.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Control, Section 6.1 – Data Handling Conventions and QC Auditing

ICH E6(R2) GCP, Section 5.1 – Quality Management and Documentation of Deviations

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Data Verification and Audit Findings

To submit study data to the FDA in CDISC SDTM format , the sponsor must map and transform the collected data from the study’s operational database (e.g., EDC) into SDTM-compliant domains .

According to GCDMP (Chapter: Standards and Data Integration) and CDISC SDTM Implementation Guide , this process includes:

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

Transforming data to comply with SDTM structural and naming conventions.

Validating the 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 A is correct — map and transform existing data to SDTM format for 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

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

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

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

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

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

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

Reference (CCDM-Verified Sources):

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

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

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

According to the Good Clinical Data Management Practices (GCDMP) , the database lock (DBL) process signifies the formal closure of the clinical trial database, ensuring that no further changes can be made to the data before statistical analysis. This process must be documented, controlled, and approved by key study stakeholders to ensure data accuracy, completeness, and readiness for analysis .

The GCDMP specifies that database lock should occur only after all data cleaning, discrepancy resolution, and reconciliation activities are complete. The lock authorization typically requires the approval of the Clinical/Scientific Representative (to confirm clinical completeness), the Data Manager (to confirm data integrity and query closure), and the Biostatistician (to confirm readiness for statistical analysis).

This tri-party approval ensures that the database reflects final, verified data consistent with the clinical protocol, and that the statistical analysis dataset derived from the database is accurate and auditable. The approval process is documented via a Database Lock Authorization Form or Sign-off Log , which becomes part of the permanent trial master file (TMF).

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Lock and Archiving, Section 7.1 – Lock Procedures and Approvals

ICH E6 (R2) GCP, Section 5.5.3 – Data Handling and Record Keeping

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Section on Database Closure

A self-evident correction (SEC) refers to a data correction that is obvious, logical, and unambiguous — such as correcting an impossible date (e.g., 31-APR-2024) or standardizing a known abbreviation (e.g., “BP” to “Blood Pressure”). According to the Good Clinical Data Management Practices (GCDMP) , SECs can be applied by data management staff following pre-approved conventions defined in the Data Management Plan (DMP) .

The DMP should explicitly describe the criteria for SECs, including the types of errors eligible for this correction method, the required documentation, and the communication procedure to inform the investigative site. The process must maintain audit trail transparency and ensure that all changes are traceable and justified .

Options A and B suggest unauthorized or informal change procedures, which violate audit and compliance standards. Option C is too restrictive, as it prevents the efficient correction of non-clinical transcription or formatting errors.

Therefore, option D is correct: “Self-evident changes may be made per the listed conventions and documented to the investigative site.” This approach aligns with CCDM expectations for balancing efficiency, accuracy, and regulatory compliance.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 6.2 – Self-Evident Corrections

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

The primary reason a statistician reviews the Case Report Form (CRF) is to ensure that the data being collected will support the planned statistical analyses for both safety and efficacy endpoints .

According to the Good Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection) , CRF design should always align with the statistical 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 the primary 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 synchronize data from electronic patient-reported outcomes (ePRO) and wearable activity-monitoring devices with site-entered visit data, both the study subject identifier and date/time are essential.

According to the GCDMP (Chapter: Data Management Planning and Study Start-up) , each dataset must contain key identifiers that allow for accurate data integration and temporal alignment . In studies involving multiple digital data sources, time-stamped subject identifiers are necessary to ensure that the device-generated data correspond to the correct subject and study visit.

The subject identifier ensures data traceability and linkage to the appropriate participant, while date/time allows synchronization of device data (e.g., activity or physiological measurements) with the corresponding site-reported visit or event. Geo-spatial data (options C and D) are typically not relevant to study endpoints and pose unnecessary privacy risks under HIPAA and GDPR guidelines.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Integration and eSource Data, Section 5.2 – Data Alignment and Synchronization Principles

FDA Guidance for Industry: Use of Electronic Health Record Data in Clinical Investigations, Section 4.2 – Data Linking and Synchronization

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

The Statistician is responsible for assessing the overall impact of data errors on the analysis and study results .

According to the Good Clinical Data Management Practices (GCDMP, Chapter: Data Quality Assurance and Control) and ICH E9 (Statistical Principles for Clinical Trials) , while the Data Manager ensures data accuracy and completeness through cleaning and validation, the Statistician determines whether the observed data discrepancies are statistically significant or if they may affect the validity, power, or interpretability of the study’s outcomes.

The Quality Auditor (C) identifies and reports issues but does not quantify analytical impact. The Investigator (D) is responsible for clinical oversight, not statistical assessment. Thus, after a database audit, the Statistician (B) performs a formal evaluation to determine whether the magnitude and nature of the errors could bias results or require reanalysis.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 7.3 – Data Audit and Impact Assessment

ICH E9 – Statistical Principles for Clinical Trials, Section 3.2 – Data Quality and Analysis Impact Assessment

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Validation and Analysis Review