Pass the SCDM Clinical Data Management CCDM Questions and answers with CertsForce

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

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

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

Reference (CCDM-Verified Sources):

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

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

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

The most effective information for addressinga backlog of unresolved queriesat investigative sites is alist of late queries by site combined with a summary table.

According to theGCDMP (Chapter: Communication and Issue Escalation), timely and structured feedback to sites is critical for efficient query resolution. A detailed list oflate or overdue queries, accompanied by summary statistics (e.g., counts, durations, status), enables data managers and monitors to prioritize follow-up actions, target problem areas, and provide focused support or retraining to underperforming sites.

While query count summaries (option B) are helpful for overview metrics, they lack the specific information (query ID, date, field, status) required for targeted follow-up. Graphs of enrollment or clean cases (options A and C) are unrelated to discrepancy resolution performance.

Thus, the combination ofdetailed lists and summarized performance metricsoffers both granularity and a high-level overview — the optimal tool for query management communication.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Communication and Issue Escalation, Section 5.1 – Site Query Management Reports

ICH E6 (R2) GCP, Section 5.18.4 – Communication Between Monitors and Sites

FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section on Query Metrics and Site Performance Review

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

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

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

When adverse events (AEs) are discovered after adatabase lock, the appropriate first step is toevaluate the impactof the missing data on theintegrity, safety analysis, and regulatory validityof the study results.

According toGCDMP (Chapter: Data Quality Assurance and Control), any post-lock data discovery requires aroot cause assessment and impact analysisbefore deciding whether to unlock the database. The key question is whether the missing AEs:

Affectprimary safety endpoints,

Introducebiasin safety reporting, or

Alterefficacy conclusions.

Based on the assessment, the Data Management and Biostatistics teams determine if unlocking and correction are justified. Simply entering data immediately (A) or repeating checks (D) without analysis may violate data control procedures.

Hence,option Bis correct — the first step is to assess theimpact on data validity and analysis.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Control, Section 5.5 – Post-Lock Findings and Impact Assessment

ICH E6(R2) GCP, Section 5.1.1 – Quality Management and Risk Assessment

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Post-Lock Data Management

The most critical consideration is thatdata cleaning is an iterative process, and completing all necessary steps — includingquery generation, site resolution, and second-pass validation— cannot realistically be accomplished within two weeks after study close.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning), data cleaning must occurcontinuously throughout the study, not only at the end. Post-database lock activities typically include running final validation checks, resolving outstanding queries, performing reconciliation (e.g., SAEs, labs, coding), and conducting final quality review.

Even in small studies,query turnaround and response cyclesfrom sites take time — typically2–4 weeks per iteration— making a two-week total cleaning period unrealistic.

Therefore,Option Dis correct: it would take more than two weeks to handle second-round (follow-up) queries and confirm final resolutions prior to database lock.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 5.4 – Ongoing vs. End-of-Study Data Cleaning

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

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

TheStatisticianis responsible for assessing theoverall impact of data errors on the analysis and study results.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Quality Assurance and Control)andICH E9 (Statistical Principles for Clinical Trials), while theData Managerensures data accuracy and completeness through cleaning and validation, theStatisticiandetermines whether the observed data discrepancies are statistically significant or if they may affect thevalidity, power, or interpretabilityof the study’s outcomes.

TheQuality Auditor (C)identifies and reports issues but does not quantify analytical impact. TheInvestigator (D)is responsible for clinical oversight, not statistical assessment. Thus, after a database audit, theStatistician (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

In anElectronic Data Capture (EDC)system, even after a data manager completes all manual queries and marks data as "clean," the data may later appearuncleanifthe site (study coordinator)makes subsequent updates in the system after re-reviewing thesource documents.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Electronic Data Capture Systems), site users maintain the authority to modify data entries as long as the system remains open for data entry. TheEDC system audit trailcaptures such changes, which can automatically invalidate prior data reviews, triggering new discrepancies or changing system edit-check statuses.

This situation commonly occurs when the site identifies corrections in the source (e.g., wrong date or lab result) and updates the EDC form accordingly. These post-cleaning changes require additional review cycles to ensure the database reflects accurate and verified information before final lock.

Options B, C, and D are incorrect — CRAs and medical monitors cannot directly change EDC data; they can only raise queries or request updates.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture Systems, Section 6.3 – Post-Cleaning Data Changes and Audit Trails

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

FDA 21 CFR Part 11 – Electronic Records: Change Documentation Requirements

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