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Title: Quiz Authoritative SCDM - CCDM - Exam Certified Clinical Data Manager Dump [Print This Page]

Author: chrisfi936    Time: yesterday 02:25
Title: Quiz Authoritative SCDM - CCDM - Exam Certified Clinical Data Manager Dump
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SCDM Certified Clinical Data Manager Sample Questions (Q82-Q87):NEW QUESTION # 82
Which document describes what study subjects expect with respect to data disclosure during and after a study?
Answer: C
Explanation:
The Informed Consent Form (ICF) is the document that explicitly describes what study subjects can expect regarding data disclosure, privacy, and confidentiality during and after participation in a clinical trial. According to ICH E6 (R2) Good Clinical Practice and FDA Human Subject Protection Regulations (21 CFR Parts 50 and 56), participants must be fully informed about how their personal and clinical data will be collected, used, stored, and shared - both during the study and in any subsequent data-sharing or publication activities.
The GCDMP reiterates that clinical data managers must ensure that all data handling practices align with the privacy commitments made in the ICF. This includes compliance with data protection regulations such as HIPAA (in the U.S.) and GDPR (in the EU). The ICF defines the permissible scope of data use, ensuring ethical management and subject protection.
Documents like the protocol or data sharing plan may outline procedures and responsibilities but do not directly inform participants of their rights and data use expectations. Only the ICF is designed for that ethical communication purpose.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Ethics, Privacy, and Data Security ICH E6 (R2) Good Clinical Practice, Sections 4.8.10 & 4.8.12 FDA 21 CFR Part 50 - Protection of Human Subjects, Informed Consent Requirements

NEW QUESTION # 83
A study is collecting ePRO assessments as well as activity-monitoring data from a wearable device. Which data should be collected from the ePRO and activity-monitoring devices to synchronize the device data with the visit data entered by the site?
Answer: A
Explanation:
To synchronize data from electronic patient-reported outcomes (ePRO) and wearable activity-monitoring devices with site-entered visit data, both the study subject identifier and date/time are essential.
According to the GCDMP (Chapter: Data Management Planning and Study Start-up), each dataset must contain key identifiers that allow for accurate data integration and temporal alignment. In studies involving multiple digital data sources, time-stamped subject identifiers are necessary to ensure that the device-generated data correspond to the correct subject and study visit.
The subject identifier ensures data traceability and linkage to the appropriate participant, while date/time allows synchronization of device data (e.g., activity or physiological measurements) with the corresponding site-reported visit or event. Geo-spatial data (options C and D) are typically not relevant to study endpoints and pose unnecessary privacy risks under HIPAA and GDPR guidelines.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Integration and eSource Data, Section 5.2 - Data Alignment and Synchronization Principles FDA Guidance for Industry: Use of Electronic Health Record Data in Clinical Investigations, Section 4.2 - Data Linking and Synchronization ICH E6 (R2) GCP, Section 5.5.3 - Data Traceability and Integrity

NEW QUESTION # 84
Which metric will identify edit checks that may not be working properly?
Answer: B
Explanation:
The best metric to identify malfunctioning or ineffective edit checks is the count by edit check of the number of times the check fired. This allows data managers to assess whether specific edit checks are performing as intended.
According to the GCDMP, Chapter: Data Validation and Cleaning, edit checks are programmed logic conditions that identify data inconsistencies or potential errors during data entry. A properly functioning edit check should trigger only when data falls outside acceptable or logical limits. If an edit check fires too frequently or not at all, it may indicate a logic error in the check's programming or configuration.
By analyzing counts by individual edit checks, data managers can:
Identify checks that never trigger (potentially inactive or incorrectly written), Detect overactive checks (poorly designed parameters causing excessive false positives), and Optimize system performance and review efficiency.
This metric supports continuous improvement in data validation logic and contributes to cleaner, higher-quality clinical databases.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.2 - Edit Check Design and Performance Metrics FDA Guidance: Computerized Systems Used in Clinical Investigations - Section on Validation of Electronic Data Systems

NEW QUESTION # 85
Which of the following statements would be BEST included in a data management plan describing the process for making self-evident corrections in a clinical database?
Answer: B
Explanation:
A self-evident correction (SEC) refers to a data correction that is obvious, logical, and unambiguous - such as correcting an impossible date (e.g., 31-APR-2024) or standardizing a known abbreviation (e.g., "BP" to "Blood Pressure"). According to the Good Clinical Data Management Practices (GCDMP), SECs can be applied by data management staff following pre-approved conventions defined in the Data Management Plan (DMP).
The DMP should explicitly describe the criteria for SECs, including the types of errors eligible for this correction method, the required documentation, and the communication procedure to inform the investigative site. The process must maintain audit trail transparency and ensure that all changes are traceable and justified.
Options A and B suggest unauthorized or informal change procedures, which violate audit and compliance standards. Option C is too restrictive, as it prevents the efficient correction of non-clinical transcription or formatting errors.
Therefore, option D is correct: "Self-evident changes may be made per the listed conventions and documented to the investigative site." This approach aligns with CCDM expectations for balancing efficiency, accuracy, and regulatory compliance.
Reference (CCDM-Verified Sources):
SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 6.2 - Self-Evident Corrections FDA 21 CFR Part 11 - Electronic Records; Audit Trails and Traceability Requirements

NEW QUESTION # 86
When implementing a study utilizing an EDC application, it would be appropriate to use free text fields for which of the following?
Answer: A
Explanation:
In Electronic Data Capture (EDC) systems, free text fields should be used only when a predefined list of acceptable responses cannot accommodate the full variability of input data - most notably for Adverse Event (AE) verbatim terms.
According to the Good Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), AE verbatim terms are initially entered as free text by site staff to accurately capture the investigator's exact medical description of the event. These verbatim terms are later coded using standardized dictionaries such as MedDRA during medical coding, ensuring both flexibility and standardization in reporting.
Conversely, fields such as urine sedimentation rate (A), date of birth (C), and Body Mass Index (D) require structured numeric or date formats to 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

NEW QUESTION # 87
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