Certified Clinical Data Manager Questions and Answers
Which is the most important reason for why a data manager would review data before a monitor reviews it?
Options:
Data managers write the Data Management Plan that specifies the data cleaning workflow.
Data can be viewed and discrepancies highlighted prior to a monitor's review.
Data managers have access to programming tools to identify discrepancies.
The GCDMP recommends that data managers review data prior to a monitor's review.
Answer:
BExplanation:
Theprimary reasondata managers review data before a monitor’s review is toidentify and flag discrepancies or inconsistenciesso that site monitors can focus their efforts more efficiently during on-site or remote source data verification (SDV).
According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning), proactive data review by data management staff ensures data completeness and accuracy by identifying missing, inconsistent, or out-of-range values. This pre-review helps streamline the monitoring process, reduces the volume of open queries, and enhances data quality.
Option A is true but not the main reason for pre-monitor review. Option C highlights a capability rather than a rationale. Option D is partially correct, but the GCDMP emphasizesprocess purpose, not prescriptive order. Thus,option Bcorrectly captures the practical and process-oriented reason for early data review—to ensure data are ready and accurate for the monitor’s review phase.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 5.3 – Data Review Timing and Purpose
ICH E6(R2) GCP, Section 5.18 – Monitoring and Data Verification Requirements
What method is used for quality control of the query resolution process?
Options:
Calculate the time from discrepancy identified to query sent.
Tabulate the number of queries sent per site.
Calculate the time from query sent to query resolution from the site.
Perform random audits of the resolved query forms.
Answer:
DExplanation:
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
An asthma study is taking into account local air quality and receives that data from the national weather bureau. Which information is needed to link research subject data to the air-quality readings?
Options:
Location identifier
Location and time identifiers
Location, time and subject identifiers
Location, time, subject and site identifiers
Answer:
BExplanation:
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
Which list should be provided to support communication with sites regarding late data and queries?
Options:
List of entered and clean data by site
List of subjects screened and enrolled by site
List of user account activity by site
List of outstanding data and queries by site
Answer:
DExplanation:
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
Which Clinical Study Report section would be most useful for a Data Manager to review?
Options:
Clinical narratives of adverse events
Enumeration and explanation of data errors
Description of statistical analysis methods
Rationale for the study design
Answer:
BExplanation:
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
Which of the following data verification checks would most likely be included in a manual or visual data review step?
Options:
Checking an entered value against a valid list of values
Checking adverse event treatments against concomitant medications
Checking mandatory fields for missing values
Checking a value against a reference range
Answer:
BExplanation:
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
Which information should be communicated by the Data Manager at regular intervals throughout a study?
Options:
Planned versus actual enrollment
Site staffing changes
Percent data entered and clean
Serious and unexpected safety events
Answer:
CExplanation:
TheData Manager (DM)plays a critical role in maintaining transparent communication with the clinical study team regardingdata quality and study progress. One of the most essential metrics regularly reported by the DM is thepercentage of data entered and cleaned.
According to theGood Clinical Data Management Practices (GCDMP, Chapter: Communication and Study Reporting), these metrics provide insight into study status, data readiness for interim analysis, and timeline predictability for database lock. Regular communication includes:
Percent of CRFs entered and verified
Percent of queries resolved
Outstanding data issues or missing pages
Other options fall outside the Data Manager’s direct responsibility:
A (Enrollment)is typically reported by clinical operations.
B (Staffing changes)are handled by site management.
D (Safety events)are communicated by the safety/pharmacovigilance team.
Thus,option Ccorrectly reflects the Data Manager’s responsibility for ongoing study communication.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Communication and Study Reporting, Section 5.3 – Study Metrics and Status Updates
ICH E6(R2) GCP, Section 5.1.1 – Communication and Oversight in Quality Management
FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Data Status Reporting
Which is the best way to identify sites with high subject attrition?
Options:
Proportion of patients for which two visit periods have passed without data by site
Number of late visits per site
Proportion of late visits by site
Number of patients for which two visit periods have passed without data
Answer:
AExplanation:
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
According to ICH E6, developing a Monitoring Plan is the responsibility of whom?
Options:
Sponsor
CRO
Data Manager
Monitor
Answer:
AExplanation:
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)
A sponsor may transfer responsibility for any or all of their obligations to a contract research organization. Which of the following statements is true?
Options:
Any written description is not transferred to the contract research organization.
A description of each of the obligations being assumed by the contract research organization is required.
A description of each of the obligations being transferred to the contract research organization is not required.
A general statement that all obligations have been transferred is acceptable.
Answer:
BExplanation:
UnderICH E6 (R2) Good Clinical Practiceand21 CFR Part 312.52, when asponsor delegates or transfers obligationsfor a clinical trial to aContract Research Organization (CRO), there must be awritten description of each specific obligation being assumed by the CRO.
According to theGood Clinical Data Management Practices (GCDMP), while sponsors may outsource responsibilities such as data management, monitoring, or biostatistics,ultimate accountability remains with the sponsor. The documentation of the transfer of responsibilities ensures regulatory transparency and compliance.
This written agreement, often referred to as aTransfer of Obligations (TOO)document, defines exactly which duties the CRO is responsible for (e.g., CRF design, data cleaning, database lock), as well as any retained sponsor oversight. A general statement that "all obligations are transferred" (option D) is insufficient per regulatory expectations, as sponsors must retain traceability of responsibility.
Therefore,Option Bis correct — a detailed written description of transferred obligations is required.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Regulatory Compliance and Oversight, Section 5.2 – Sponsor and CRO Responsibilities
ICH E6 (R2) Good Clinical Practice, Section 5.2.1 – Transfer of Trial-Related Duties and Functions
FDA 21 CFR 312.52 – Transfer of Obligations to a Contract Research Organization
Which of the following laboratory findings is a valid adverse event reported term that facilitates auto coding?
Options:
Elevated HDL
ALT
Abnormal SGOT
Increased alkaline phosphatase, increased SGPT, increased SGOT, and elevated LDH
Answer:
AExplanation:
When coding adverse events (AEs) usingMedDRA (Medical Dictionary for Regulatory Activities), valid AE terms must correspond to specific, medically meaningful concepts thatmatch directly to a Preferred Term (PT)orLowest Level Term (LLT)in the dictionary.
Among the options,“Elevated HDL”(High-Density Lipoprotein) represents a single, medically interpretable, and standard term that can directly match to a MedDRA LLT or PT. This makes it suitable forauto-coding, where the system automatically maps verbatim terms to MedDRA entries without manual intervention.
In contrast:
ALT (B)andAbnormal SGOT (C)are incomplete or nonspecific; they describe test names or qualitative interpretations rather than events.
Option Dlists multiple findings, making it too complex for automatic mapping. Such compound entries would requiremanual coding review.
According toGCDMP (Chapter: Medical Coding and Dictionaries), a valid AE term should be:
Clinically interpretable(not just a lab test name)
Unambiguous
Single-concept based, not a collection of results
Thus,option A (Elevated HDL)is correct, as it aligns with MedDRA’s single-concept, standard terminology structure suitable for auto-coding.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Medical Coding and Dictionaries, Section 5.3 – Auto-coding and Verbatim Term Management
ICH M1 MedDRA Term Selection: Points to Consider, Section 2.1 – Coding Principles
ICH E2B(R3) – Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports
A Data Manager is designing a report to facilitate discussions with sites regarding late data. Which is the most important information to display on the report to encourage sites to provide data?
Options:
Number of forms entered in the last week
Expected versus actual forms entered
List of outstanding forms
Total number of forms entered to date
Answer:
CExplanation:
In managingsite data timeliness, the most actionable and effective tool is areport listing all outstanding (missing or incomplete) CRFs.
According toGCDMP (Chapter: Communication and Study Reporting), Data Managers must providesite-level performance reportshighlighting:
Outstanding CRFs not yet entered,
Unresolved queries, and
Pending data corrections.
Such reports help sites prioritize and address data gaps efficiently.
Option AandDare historical metrics without actionable context.
Option Bgives a general overview but lacks specific site-level actionability.
Hence,option C (List of outstanding forms)provides the clearest and most motivating feedback to sites for timely data entry and query resolution.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Communication and Study Reporting, Section 5.3 – Data Timeliness and Reporting Metrics
ICH E6(R2) GCP, Section 5.1.1 – Sponsor Oversight and Data Communication Requirements
FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Site-Level Data Timeliness Reporting
An organization has completed a study and wants to submit the data to the FDA using CDISC SDTM. Which of the following must be done?
Options:
Map and transform the study data to SDTM
Re-enter the data into an SDTM compliant system
Provide a letter of intent to use SDTM to the FDA
SDTM cannot be used in this situation
Answer:
AExplanation:
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
An organization conducts over fifty studies per year. Currently each study is specified and set-up from scratch. Which of the following organizational infrastructure options would streamline database set-up and study-to-study consistency?
Options:
Adopting an ODM compliant database system
Maintaining a library of form or screen modules
Improving the form or screen design process
Implementing controlled terminology for adverse events
Answer:
BExplanation:
To improve efficiency and ensure consistency across multiple studies, the most effective infrastructure solution is tomaintain a centralized library of standardized forms or screen modules(e.g., CRF/eCRF templates).
According to theGood Clinical Data Management Practices (GCDMP, Chapter: Database Design and Build), using aform libraryallows reuse of validated data collection modules for commonly collected domains such as demographics, adverse events, and vital signs. This reduces database setup time, enhances uniformity in data definitions, and ensures alignment with standards such asCDISC CDASH and SDTM.
While adoptingODM (A)provides standardized data exchange and interoperability, it does not inherently reduce setup workload.Improving design processes (C)enhances efficiency but doesn’t guarantee consistency, andimplementing controlled terminology (D)helps with coding standardization, not database structure.
Therefore,option B—maintaining a library of form or screen modules— provides the most direct and sustainable improvement for scalability and quality.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Database Design and Build, Section 5.3 – Use of Standard Libraries and Templates
CDISC CDASH Implementation Guide, Section 3.2 – Reusable CRF Modules and Standardization
ICH E6(R2) GCP, Section 5.5.3 – Standardization and Reuse in Data Collection Systems
Which document contains the details of when, to whom, and in what manner the vendor data will be sent?
Options:
Project Plan
Communication Plan
Data Transfer Agreement
Data Management Plan
Answer:
CExplanation:
AData Transfer Agreement (DTA)defines the operational and technical details for transferring data between a sponsor and an external vendor (e.g., central lab, ECG vendor). It is a formalized, controlled document specifyingwhat data will be sent, when transfers will occur, the transfer method, file structure, encryption or security protocols, and the recipients of the data.
The DTA is developed jointly by the sponsor and vendor before production data transfers begin. According to theGCDMP, Chapter on External Data Transfers, this agreement ensures both parties share a clear understanding of timing, responsibility, and data content to minimize errors and ensure regulatory compliance.
TheData Management Plan (DMP)outlines general data handling processes but does not capture the technical specifics of vendor data transfer logistics. TheProject Plan (A)andCommunication Plan (B)are broader operational tools and not specific to data transfer protocols.
Hence,option C (Data Transfer Agreement)is the correct answer, as it precisely governs the procedural and technical framework of vendor data exchange.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: External Data Transfers, Section 4.1 – Data Transfer Agreements and Specifications
ICH E6(R2) Good Clinical Practice, Section 5.5 – Trial Management, Data Handling, and Record Keeping
Which statement is true regarding User Acceptance Testing (UAT) in an EDC application?
Options:
System tools in EDC do not remove the need for UAT
Data should not be collected in a production environment until UAT is completed
Every rule should be tested with at least one "pass" and one "fail" scenario
The extent of UAT (i.e., the number of test cases and rules) cannot be risk-based
Answer:
BExplanation:
InElectronic Data Capture (EDC)system validation,User Acceptance Testing (UAT)is a mandatory phase that must becompleted before data collection begins in the production environment.
According to theGCDMP (Chapter: Database Design, Validation, and Testing)andFDA 21 CFR Part 11, UAT ensures that the EDC system meets allprotocol-specific, functional, and regulatory requirementsbefore it is deployed for live use. The goal is to verify that the system performs exactly as intended by simulating real-world user interactions withtest datain avalidated test environment.
Data collection prior to UAT completion would violate validation requirements and risk noncompliance withICH E6 (R2) GCP Section 5.5.3, which mandates that all computerized systems be validated and tested before use.
While options A and C describe correct components of testing strategy,the key regulatory requirementis thatUAT must be completed and approved before live data entry begins. Option D is incorrect — risk-based UAT is an accepted modern validation approach under bothFDA and GAMP5principles.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Validation, Section 5.3 – User Acceptance Testing
FDA 21 CFR Part 11 – Validation of Electronic Systems (Section 11.10(a))
ICH E6 (R2) GCP, Section 5.5.3 – Validation Before Use in Production Environment
Which of the following scenarios requires a query to be sent to the central lab first when there is a discrepancy between the final lab data transfer and the CRF?
Options:
Both the central lab and the CRF have data present for a visit
The CRF has data for a visit but the central lab has missing data for the visit
The central lab has data for a visit but the CRF has missing data for the visit
Both the central lab and the CRF data have missing data for a visit
Answer:
CExplanation:
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
A study uses commercially available activity monitors and collects data for each patient weekly by selecting and downloading the data from the manufacturer's website. There are 100 patients in the study and it takes the Data Manager 20 minutes per file to download, import, and process the data. Assuming that the distribution of work is uniform over the six-month trial, how many Data Managers are needed for the activity data alone?
Options:
Ten percent of a Data Manager per month
Fifty percent of a Data Manager per month
Two Data Managers per month
One Data Manager per month
Answer:
DExplanation:
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 a hospitalized subject in a cardiovascular trial experiences a repeated but mild episode of tachycardia, the physician decides to extend the subject's hospital stay for continued observation. How would this event be characterized?
Options:
Serious adverse event
Adverse event
Severe adverse event
Spontaneous adverse event
Answer:
AExplanation:
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
A statistician analyzes data from a randomized, double-blind, placebo-controlled study and finds that the placebo outperformed the investigational product. Which of the following is a plausible explanation for this?
Options:
The placebo was intended to contain medicinal properties.
Sites appropriately dispensed the investigational product to the subjects.
The treatment codes were incorrectly entered into the database.
The investigational product performed well in this study population.
Answer:
CExplanation:
In arandomized, double-blind, placebo-controlled study, if statistical analysis shows that theplacebo appears to outperform the investigational product, a likely cause is adata management or coding error, particularly intreatment code entry or mapping.
According to theGCDMP (Chapter: Database Design and Build), treatment assignment data — typically stored in randomization or code-break files — must beaccurately integratedinto the clinical database. Any mismatch between randomization codes, subject identifiers, or treatment arms can lead to incorrect grouping during analysis, producing false conclusions such as placebo superiority.
The Data Manager should initiate aroot cause reviewof randomization data integration and treatment mapping. The placebo is never designed to have active medicinal effects (option A). Option D is incorrect because the described scenario implies a data inconsistency, not true efficacy differences. Proper verification of randomization coding and reconciliation between data management and statistical programming systems are essential.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Build, Section 6.1 – Randomization and Treatment Code Management
ICH E6 (R2) GCP, Section 5.5.3 – Data Verification and Coding Accuracy
FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Mapping and Validation Requirements
Which of the following factors can be tested through a second test transfer?
Options:
Change management
File format
Transfer method
Transfer frequency
Answer:
BExplanation:
In the context ofdatabase design and external data management, atest data transfer(or trial data load) is performed to ensure the proper configuration, structure, and integrity of data imported from an external vendor or system. Thesecond test transferis specifically useful to confirm thatdata structures and formatsare consistently aligned between the sending and receiving systems after initial adjustments have been made from the first test.
According to theGood Clinical Data Management Practices (GCDMP), thefile format— including variables, data types, field lengths, delimiters, and encoding — must be validated during test transfers to confirm compatibility and ensure accurate loading into the target database. Once the initial test identifies and corrects errors (e.g., mismatched variable names or data types), the second transfer verifies that the corrections have been implemented correctly and that the file structure functions as intended.
Testing change management (A) involves procedural controls, not data transfers. Thetransfer method (C)andtransfer frequency (D)are validated during initial process setup, not during subsequent test transfers.
Therefore,option B (File format)is correct, as the second test transfer verifies the technical integrity of the file structure before live production transfers begin.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: External Data Transfers and Data Integration, Section 5.2 – Test Transfers and File Validation
FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – Data Import and Validation Controls
In development of CRF Completion Guidelines (CCGs), which is a minimum requirement?
Options:
CCGs are designed from the perspective of the Study Biostatistician to ensure that the data collected can be analyzed
CCGs must be signed before database closure to include all possible protocol changes affecting CRF completion
CCGs must include a version control on the updated document
CCGs are developed with representatives of Data Management, Biostatistics, and Marketing departments
Answer:
CExplanation:
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
Which of the following roles commonly requires data entry and update privileges in an EDC application used in a clinical study?
Options:
Site Study Coordinator
Clinical Study Monitor
EDC System Administrator
Study Statistician
Answer:
AExplanation:
In an EDC system,Site Study Coordinatorsare typically responsible fordata entry and updates, as they are the site-level personnel who record subject data from source documents into the electronic CRFs (eCRFs).
TheGood Clinical Data Management Practices (GCDMP, Chapter: EDC Systems)outlines that data entry and modification privileges should only be granted toqualified site personnelwho have completed EDC system training and are listed on the study delegation log. These users directly handle patient-level data entry and correction.
In contrast:
Clinical Study Monitors (B)review and verify data but do not enter or modify it.
EDC System Administrators (C)manage user access and configuration settings, not study data.
Study Statisticians (D)work with extracted, cleaned datasets but never have data modification privileges.
Thus,option A (Site Study Coordinator)correctly identifies the role with authorized data entry and update privileges.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Electronic Data Capture (EDC) Systems, Section 5.2 – User Roles and Access Permissions
ICH E6(R2) GCP, Section 4.1 – Investigator Responsibilities for Data Accuracy
FDA 21 CFR Part 11 – User Access and Accountability in Electronic Systems
A study takes body-composition measurements at baseline using a DEXA scanner. Which information is needed to correctly associate the body-composition data to the rest of the study data?
Options:
Study number and subject number
Subject number
Study number and visit number
Subject number and visit number
Answer:
DExplanation:
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
A study collects blood pressure. Which is the best way to collect the data?
Options:
Coding a verbatim field with a MedDRA diagnosis
Two continuous variables
High/Low radio button
Check boxes for twenty-point increments
Answer:
BExplanation:
Blood pressure is aquantitative physiological measurement, typically consisting oftwo continuous numeric values: systolic and diastolic pressure. Therefore, the most appropriate and scientifically valid method of data collection is to usetwo continuous variables(e.g., systolic = 120 mmHg, diastolic = 80 mmHg).
According to theGCDMP (Chapter: CRF Design and Data Collection), data fields must be designed to capture the mostprecise, accurate, and analyzableform of clinical data. Numeric data should be collected using numeric data types to allow for range checks, calculations (e.g., mean arterial pressure), and statistical analysis.
Options such as categorical representations (radio buttons or check boxes) introduce rounding, data loss, and analytic limitations. Coding a verbatim diagnosis (option A) is inappropriate for numeric vital sign data and violates the principle of capturing data at the most granular level.
Thus, the correct and validated method per CCDM standards istwo continuous variables, ensuring accuracy, traceability, and analytical flexibility.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 4.2 – Best Practices for Quantitative Data Capture
ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Accuracy and Collection Standards
FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 4.3 – Data Format and Structure Requirements
In a study conducted using paper CRFs, a discrepancy is discovered in a CRF to database QC audit. What is the reason why this discrepancy would be considered an audit finding?
Options:
Discrepancy not explained by the protocol
Discrepancy not explained by the CRF completion guidelines
Discrepancy not explained by the data handling conventions
Discrepancy not explained by the data quality control audit plan
Answer:
CExplanation:
In aCRF-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 thatcannot be explained by documented data handling conventions, they are classified asaudit findings.
PerGCDMP (Chapter: Data Quality Assurance and Control),data handling conventionsdefine 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
Which is a minimum prerequisite that should be in place before choosing an EDC system?
Options:
Knowledge of functional requirements
Completed installation qualification
Updated governance documentation
Draft validation plan
Answer:
AExplanation:
Before selecting anElectronic Data Capture (EDC)system for a clinical trial, it is essential to have a clear understanding of thefunctional requirements. This serves as theminimum prerequisiteto guide system selection, ensuring that the EDC solution aligns with the protocol needs, data workflow, security requirements, and regulatory compliance.
According to theGood Clinical Data Management Practices (GCDMP, Chapter: Computerized Systems and Compliance), functional requirements describe what the system must do—such as data entry capabilities, edit checks, query management, user roles, audit trails, and integration with external systems (e.g., labs, ePRO). This understanding allows sponsors and CROs to evaluate vendor systems effectively during the selection and qualification phase.
Other options:
B. Installation qualificationandD. Validation planoccuraftersystem selection.
C. Governance documentationsupports operations but is not required before choosing the system.
Hence,option Ais correct — the first and most essential prerequisite before EDC selection is a solid understanding of thefunctional requirements.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Computerized Systems and Compliance, Section 4.2 – Requirements Gathering and System Selection
FDA 21 CFR Part 11 – System Validation and Intended Use Requirements
ICH E6(R2) GCP, Section 5.5.3 – Computerized System Selection and Qualification
A Data Manager is asked to manage SOPs for a department. Given equal availability of the following systems, which of the following is the best choice for managing the organizational SOPs?
Options:
Document management system
Customized Excel spreadsheet
Learning management system
Existing paper filing system
Answer:
AExplanation:
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
Based on the project Gantt chart as of 01 Nov 2019, an interim analysis is scheduled to occur early Q2 of 2020. All of the following are valid for initially assessing the status of data cleanliness EXCEPT:
Options:
Determining CRF data entry status of received pages
Identifying missing pages where visits have been completed to date
Identifying the number of discrepancies resolved to date
Identifying all outstanding discrepancies to date and aging
Answer:
CExplanation:
When initially assessingdata cleanlinessin preparation for aninterim analysis, the focus should be onoutstanding issuesthat could affect data completeness and reliability.
According to theGCDMP (Chapter: Data Quality Assurance and Control), key indicators of readiness include:
TheCRF data entry statusof received pages (option A) to confirm completeness.
Identification ofmissing pages or visits(option B) to verify subject-level completeness.
A listing ofoutstanding discrepancies and their aging(option D) to assess unresolved data issues.
Counting the number ofdiscrepancies resolved to date (option C), however, does not reflect data quality or current data readiness—it indicates past actions rather than current unresolved risks. Therefore, it isnot a valid measurefor assessing interim data cleanliness.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 6.1 – Data Readiness Assessments for Analysis
ICH E6 (R2) GCP, Section 5.18.4 – Ongoing Data Quality Review
FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section 7 – Data Quality Indicators
A Clinical Data Manager reads a protocol for a clinical trial to test the efficacy and safety of a new blood thinner for prevention of secondary cardiac events. The stated endpoint is all-cause mortality at 1 year. Which data element would be required for the efficacy endpoint?
Options:
Drug level
Coagulation time
Cause of death
Date of death
Answer:
DExplanation:
The efficacy endpoint ofall-cause mortality at one yeardirectly depends on thedate of deathfor each subject, makingOption D – Date of deaththe required data element.
According to theGCDMP (Chapter: Clinical Trial Protocols and Data Planning)andICH E3/E9 Guidelines, the primary efficacy analysis must be based on time-to-event data, particularly when the endpoint involvesmortality or survival. Thedate of deathallows accurate calculation oftime from randomization to event, essential for survival analysis (e.g., Kaplan-Meier curves).
Whilecause of death (C)may be collected for safety or secondary analyses,all-cause mortalityspecifically includes any death regardless of cause.Drug levels (A)andcoagulation times (B)may serve as pharmacodynamic or exploratory endpoints but do not directly measure mortality.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Protocol Review, Section 5.4 – Defining Data Required for Endpoints
ICH E9 – Statistical Principles for Clinical Trials, Section 2.3 – Time-to-Event Endpoints
FDA Guidance for Industry: Clinical Trial Endpoints for Drug Development and Approval
For clinical investigational sites on an EDC trial, which of the following archival options allows traceability of changes made to data?
Options:
Storing the computer used at the clinical investigational site
Paper copies of the source documents
PDF images of the final eCRF screens for each patient
ASCII files of the site's data and related audit trails
Answer:
DExplanation:
Regulatory agencies such as theFDAandICHrequire thatelectronic data be retained in a format that preserves audit trails and traceability.
While PDF images (option C) provide a static representation of data, they do not preserve theunderlying audit trail(i.e., who changed what, when, and why). TheASCII data files with corresponding audit trails(option D) provide complete transparency and comply with21 CFR Part 11andGCDMParchival standards.
Option A(storing computers) is unnecessary and impractical, andOption B(paper source documents) are site records, not system archives.
Hence,option Dis correct —ASCII data files with audit trailsmeet traceability and compliance standards.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.4 – Archival Formats and Audit Trail Retention
ICH E6(R2) GCP, Section 5.5.3 – Data Integrity, Audit Trails, and Record Retention
FDA 21 CFR Part 11 – Electronic Records; Audit Trail and Retention Requirements
An international study collects lab values. Sites use different units in the source documents. Which of the following data collection strategies will have fewer transcription errors?
Options:
Allow values to be entered as they are in the source document and derive the units based on the magnitude of the value
Allow values to be entered as they are in the source and the selection of units on the data collection form
Use a structured field and print standard units on the data collection form
Have all sites convert the values to the same unit system on the data collection form
Answer:
BExplanation:
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
A study team member states that data entry can be done by clerical personnel at sites. Which are important considerations?
Options:
It is possible that clerical personnel could be hired by sites because data entry requires little training and use of clerical personnel would reduce burden on sites
Historically in clinical research site study coordinator roles have been filled by people with clinical or clinical research experience
Data entry at sites requires study-specific training on how to use the EDC system to enter data and respond to data discrepancies identified by the system
The person at the sites who enters the data usually also understands which data in the medical record are needed for the study, where to find them and which value to choose
Answer:
CExplanation:
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
Which method would best identify clinical chemistry lab data affected by a blood draw taken distal to a saline infusion?
Options:
Abnormally high sodium values in a dataset
Lab values from a blood draw with a very high sodium and very low other values
Abnormally low urine glucose values in a dataset
Lab values from a blood draw with a very low sodium and very high other values
Answer:
BExplanation:
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
A protocol is updated mid-study to add an additional procedure about which data needs to be collected. Which of these statements applies?
Options:
The DMP should be updated to reflect the changes to the protocol, but this update does not need to be communicated
The DMP should be updated to reflect the changes to the protocol and stakeholders notified
The DMP does not need to be updated as it represents the data at the beginning of the trial only
The DMP does not need to be updated until the end of the trial and all updates are included in the DMP to indicate what happened in the trial
Answer:
BExplanation:
When aprotocol is amended mid-study, resulting in additional data collection requirements, theData Management Plan (DMP)must beupdated accordinglyand all relevant stakeholders must benotified.
According to theGCDMP (Chapter: Data Management Planning and Study Start-up), the DMP is aliving documentthat defines all data management processes for a clinical study. It must accurately reflect thecurrent data flow, CRF design, validation procedures, and reporting structure. Any protocol amendments affecting data capture, structure, or analysis require immediate DMP revision and distribution to ensure alignment across data management, clinical, and biostatistics teams.
Failure to update and communicate DMP changes can lead to misalignment in data handling and introduce compliance risks during audits or inspections. Therefore,Option Bis correct: the DMP must be updated and the change communicated to all stakeholders (e.g., sponsor, CRO, clinical operations, biostatistics).
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Plan (DMP), Section 5.3 – Maintaining and Updating the DMP
ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Documentation of Protocol Changes and Data Handling Procedures
FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Section on Data Management Documentation
Which of the following is a best practice for creating eCRFs for a study?
Options:
Set up coded terms so they are available to the site user
Set up features that automatically enter data into fields when bypassed
Develop eCRFs with cross-functional team members
Develop eCRFs that closely follow paper CRF standards
Answer:
CExplanation:
Thebest practicefor developingelectronic Case Report Forms (eCRFs)is to involvecross-functional team membersthroughout the design process.
According to theGCDMP (Chapter: CRF Design and Data Collection), eCRFs should be collaboratively developed bydata management, clinical operations, biostatistics, medical, and regulatory teams. Each function provides a unique perspective — data managers focus on data capture and validation; statisticians ensure alignment with analysis requirements; clinicians ensure medical relevance and protocol compliance.
Collaborative development ensures that the eCRFs arefit-for-purpose, capturing all required data accurately, minimizing redundancy, and supporting downstream data analysis.
Options A and B violate good data management practice because sites should not directly access coded terms (to prevent bias), and fields shouldnever auto-populate without explicit source verification. Option D is outdated; while paper CRFs may inform structure,EDC-optimized eCRFsshould leverage system functionality rather than mimic paper.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 4.2 – Collaborative CRF Development
ICH E6 (R2) GCP, Section 5.5.3 – Data Collection and System Validation
FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 3.4 – CRF Design Considerations
Who has primary responsibility for ensuring accurate completion of the CRF?
Options:
Clinical Data Manager
Site Coordinator
Clinical Research Associate
Investigator
Answer:
DExplanation:
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
The primary reason for system validation is to:
Options:
Allow a system to be used by its intended users.
Fulfill the validation plan.
Meet regulatory requirements.
Prove the system being tested works as intended.
Answer:
DExplanation:
Theprimary purpose of system validationin clinical data management is todemonstrate and document that the computerized system performs as intended—accurately, reliably, and consistently—throughout its lifecycle.
According to theGood Clinical Data Management Practices (GCDMP, Chapter on System Validation)andFDA 21 CFR Part 11, validation ensures that all system functions (e.g., data entry, edit checks, audit trails, security) work as designed, providing data integrity, traceability, and regulatory compliance. The focus is onfitness for intended use, meaning the system reliably produces correct and reproducible results in the context of its operational environment.
While meeting regulatory requirements (option C) and fulfilling a validation plan (option B) are components of the process, they arenot the ultimate purpose. The essential goal is ensuring that the system performs as intended, maintainingaccuracy and data integrityfor clinical trial operations.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Computerized Systems and System Validation, Section 5.2 – Purpose and Scope of System Validation
FDA 21 CFR Part 11 – Validation of Computerized Systems for Intended Use
ICH E6(R2) GCP, Section 5.5.3 – Computerized System Validation and Data Integrity
A site study coordinator attempts to make an update in a study database in an EDC system after lock. What occurs?
Options:
The old value is replaced in all locations by the new value
The change is approved by the Data Manager before it is applied
The site study coordinator is not able to make the change
The change is logged as occurring after lock
Answer:
CExplanation:
Once a clinical database islocked, it becomesread-only— no further data modifications can be made by any users, including site personnel. This ensures that the data arefinalized, consistent, and auditablefor statistical analysis and regulatory submission.
According to theGCDMP (Chapter: Database Lock and Archiving), the lock process involves freezing the database to prevent accidental or unauthorized changes. After lock, access permissions are restricted, and all edit and update functions are disabled. If any corrections are required post-lock, the database must beunlocked under controlled procedures(with full audit trail documentation).
Thus,option C—The site study coordinator is not able to make the change— correctly reflects standard EDC functionality and regulatory compliance.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.2 – Database Lock Procedures and Controls
ICH E6(R2) GCP, Section 5.5.3 – Data Integrity and Audit Trail Requirements
FDA 21 CFR Part 11 – Controls for Electronic Records and System Lock Functions
QA is conducting an audit on a study for ophthalmology which is ready for lock. Inconsistencies are found between the database and the source. Of the identified fields containing potential data errors, which fields are considered critical for this particular study?
Options:
Subject Identifier
Concomitant Medications
Weight
Medical History
Answer:
BExplanation:
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)
Which method would best identify inaccuracies in safety data tables for an NDA?
Options:
Compare counts of appropriate patients from manual CRFs to counts in table cells
Compare counts of appropriate patients from line listings of CRF data to counts in table cells
Review the tables to identify any values that look odd
Review the line listings to identify any values that look odd
Answer:
BExplanation:
The best method for identifying inaccuracies in safety data tables prepared for aNew Drug Application (NDA)is tocompare counts of appropriate patients from line listings of CRF data to the counts in table cells.
According to theGCDMP (Chapter: Data Quality Assurance and Control), line listings representraw, patient-level dataextracted directly from the clinical database, whereas summary tables areaggregated outputsused for reporting and submission. Comparing these two sources ensuresdata traceability and accuracy, verifying that tabulated results correctly reflect the underlying patient data.
Manual CRF checks (option A) are less efficient and error-prone, as data entry is typically already validated electronically. Simply reviewing tables or listings for “odd values” (options C and D) lacks the systematic verification necessary for regulatory data integrity.
Thus,comparing line listings to tables (option B)provides a quantitative cross-check between the database and output deliverables, a standard practice in NDA data validation and statistical quality control.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 5.2 – Validation of Tables, Listings, and Figures (TLFs)
FDA Guidance for Industry: Submission of NDA Safety Data, Section on Data Verification and Accuracy
ICH E6 (R2) GCP, Section 5.5.3 – Validation of Derived Data Outputs
The Scope of Work would answer which of the following information needs?
Options:
To look up which visit PK samples are taken
To look up the date of the next clinical monitoring visit for a specific site
To determine the number of database migrations budgeted for a project
To find the name and contact information of a specific clinical data associate
Answer:
CExplanation:
TheScope of Work (SOW)is a contractual document that outlines thespecific deliverables, responsibilities, timelines, and budgetary detailsfor a given project between the sponsor and the contract research organization (CRO).
According to theGood Clinical Data Management Practices (GCDMP, Chapter: Project Management and Communication), the SOW defineswhat work will be performed,how many resources are allocated, andthe expected deliverables. This includes detailed information such as:
The number of database builds or migrations,
Timelines for deliverables (e.g., database lock),
Responsibility distribution between sponsor and CRO, and
Budget parameters for defined activities.
Therefore, if a Data Manager needs to determinehow many database migrations are budgeted for a project, theSOWis the correct document to reference.
Information such as PK sample scheduling (option A), site monitoring dates (option B), or staff contact details (option D) would be found in operational plans or contact lists, not in the SOW.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Project Management and Communication, Section 6.2 – Scope of Work Definition and Deliverables
ICH E6 (R2) GCP, Section 5.5.3 – Documentation and Responsibilities for Data Management Tasks
FDA Guidance for Industry: Oversight of Clinical Investigations – Sponsor and CRO Agreements
A study numbers subjects sequentially within each site and does not reuse site numbers. Which information is required when joining data across tables?
Options:
Subject number and site number
Subject number
Study number and subject number
Site number
Answer:
AExplanation:
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
For a study, body mass index is calculated from weight and height. Which information is needed to document the transformation?
Options:
Algorithm and algorithm version associated with the calculated value
Algorithm associated with the calculated value
User ID making the change and reason for change
Algorithm documented in the Data Management Plan
Answer:
AExplanation:
When derived or calculated variables (likeBody Mass Index) are created, it is essential to document thealgorithmused and itsversionto ensure full data traceability and reproducibility.
According toGCDMP (Chapter: Database Design and Derived Data), every derived field must include metadata describing:
Thederivation algorithm(e.g., BMI = weight [kg] / height² [m²])
Theversionof the algorithm (if updates or revisions occur)
Any associateddata sourcesor transformation rules
This ensures consistent calculation across systems, prevents discrepancies during regulatory submissions, and aligns withFDAandCDISCdocumentation expectations.
Option B lacks version control, which is critical for traceability. Option C describes audit trail data (not derivation metadata), and option D refers to broader documentation, not specific algorithm traceability.
Hence,option A (Algorithm and algorithm version associated with the calculated value)is the correct and compliant answer.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Derived Data and Algorithms, Section 5.3 – Documentation and Metadata Requirements
ICH E6(R2) GCP, Section 5.5.3 – Derived Data and Validation Traceability
FDA Guidance for Industry: Providing Regulatory Submissions in Electronic Format – Data Definitions (Define.xml)
During testing of an ePRO system, a test fails. Which information should be found in the validation documentation?
Options:
Training requirements
Expected and actual results
Reconciliation datapoints
Root cause analysis of the system errors
Answer:
BExplanation:
When a system validation test fails duringElectronic Patient-Reported Outcome (ePRO)system testing, thevalidation documentationmust record theexpected results(what should have occurred) and theactual results(what occurred).
According to theGCDMP (Chapter: Database Validation and Testing), proper system validation documentation ensurestraceability, reproducibility, and compliancewithFDA 21 CFR Part 11andICH E6 (R2). Each test case must include:
Test objective,
Preconditions,
Test steps,
Expected results,
Actual results, and
Pass/fail status.
If a test fails, this documentation provides the objective evidence necessary for deviation handling, issue resolution, and re-testing. While a separateroot cause analysismay be performed later (option D), the validation record itself must focus on verifying outcomes against predefined expectations.
Therefore, the correct answer isB – Expected and actual results.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Validation and Testing, Section 4.4 – Documentation of Test Results
FDA 21 CFR Part 11 – Validation Requirements (Section 11.10(a))
ICH E6 (R2) GCP, Section 5.5.3 – Computer System Validation and Documentation
