CCDM Exam Dumps - Certified Clinical Data Manager

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

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

When theprimary efficacy endpointin a clinical trial is3-month survival, the key data element required is thedeath date. This is because the survival endpoint is determined by calculating whether the subject lived or died within a defined time frame from study enrollment or randomization.

According to theGCDMP (Chapter: Data Management Planning and Study Start-up), the Clinical Data Manager (CDM) must identify and ensure the capture of allcritical data elementsnecessary to evaluate the study endpoints. For time-to-event analyses (e.g., survival studies), accurateevent dates (death date)are essential for endpoint derivation and statistical analysis.

Other data elements such as cause of death or date of autopsy (options B and C) may support secondary analyses or safety reviews but are not necessary to determine the survival endpoint itself. Similarly,birth date(option D) contributes to demographic data but is unrelated to the primary efficacy outcome.

Reference (CCDM-Verified Sources):

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

ICH E9 – Statistical Principles for Clinical Trials, Section 2.2.3 – Time-to-Event Data Considerations

FDA Guidance for Industry: Clinical Trial Endpoints for Drug Development

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

The primary reason astatistician reviews the Case Report Form (CRF)is to ensure thatthe data being collected will support the planned statistical analysesfor bothsafety and efficacy endpoints.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), CRF design should always align with thestatistical analysis plan (SAP)to ensure that all necessary data elements are collected accurately and in analyzable formats. The statistician verifies that the CRF captures:

All endpoints specified in the protocol

Proper derivation or calculation fields

Timing of assessments

Consistency across visits and forms

Options B, C, and D address secondary or technical design considerations but not theprimary analytical purpose. The review ensures that the CRF provides a complete and analyzable dataset for meeting study objectives, regulatory submissions, and statistical integrity.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: CRF Design and Data Collection, Section 4.4 – Role of Statistics in CRF Design

ICH E9 – Statistical Principles for Clinical Trials, Section 5.2 – Data Collection and Analysis Alignment

FDA Guidance for Industry: E6(R2) GCP, Section 5.1 – Quality Management and Design Input from Stakeholders

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

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

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

Reference (CCDM-Verified Sources):

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

FDA 21 CFR Part 11 – System Validation Requirements

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

Maintainingtraceabilityof external data imports (such as laboratory results) is a fundamental principle of clinical data management. According to theGCDMP (Chapter: External Data Transfers and Integration), Data Managers mustretain an unaltered copy of the raw data exactly as received from the vendor.

This archived version serves as a reference for:

Data provenance verification,

Audit trail review, and

Discrepancy resolution between vendor and study database.

Since the central lab maintains its own audit trail, the Data Manager’s responsibility is to preserve theoriginal data transmission filebefore applying transformations, merges, or validations.

Options A, C, and D describe procedural safeguards but do not meet the regulatory requirement oftraceable data lineage. Onlyoption B (Maintaining a copy of the data as received)ensures compliance withICH E6(R2)andFDA 21 CFR Part 11standards for data traceability and integrity.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers and Integration, Section 5.2 – Data Traceability and Version Control

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

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

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