Free Practice Questions for SCDM CCDM Exam

InElectronic Data Capture (EDC)systems, free text fields should be usedonly when a predefined list of acceptable responses cannot accommodate the full variability of input data— most notably forAdverse Event (AE) verbatim terms.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), AE verbatim terms are initially entered asfree textby site staff to accurately capture the investigator’s exact medical description of the event. These verbatim terms are latercodedusing standardized dictionaries such asMedDRAduring medical coding, ensuring both flexibility and standardization in reporting.

Conversely, fields such asurine sedimentation rate (A),date of birth (C), andBody Mass Index (D)requirestructured numeric or date formatsto enable validation, range checks, and consistency across datasets. Free text would compromise data integrity, accuracy, and validation efficiency for these structured data elements.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 4.3 – Use of Free Text and Coded Fields

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

MedDRA Introductory Guide, Section 2.3 – Verbatim Entry and Coding Requirements

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)

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

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

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

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

Reference (CCDM-Verified Sources):

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

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

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

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

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

Examples of front-end checks include:

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

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

Requiring mandatory fields to be completed before moving forward.

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

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

Reference (CCDM-Verified Sources):

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

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

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

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

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

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

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

SAE reconciliationmust becompleted before database lock or closureto ensure all safety data are consistent between theclinical databaseand thepharmacovigilance (safety) database.

According to theGCDMP (Chapter: Safety Data Handling and Reconciliation), SAE reconciliation involves verifying that all adverse events reported in the clinical trial database are also captured and accurately recorded in the safety system (and vice versa). This is essential to confirm thatno SAE is missing, misclassified, or inconsistently dated or codedbetween the two systems.

Performing this reconciliation before database lock ensures that any discrepancies are corrected, and both databases reflect consistent, verified information for regulatory submission. Conducting this after closure (or only at audit time) would risk data inconsistencies in the final submission datasets.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: SAE Reconciliation, Section 6.1 – Timing and Procedures for Reconciliation

ICH E2A/E2F – Clinical Safety Data Management: Definitions and Standards

FDA Guidance for Industry: E2A – Clinical Safety Data Management: Processing Standards for Safety Reports

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