A study uses and collects pacemaker interrogation data for each patient weekly by selecting and downloading the data from the manufacturer's website. There are 200 patients in the study and it takes the Data Manager 30 minutes per file to download, import, and process the dat
Answer(s): B
Let's calculate the workload:200 patients × 30 minutes = 6,000 minutes/week6,000 minutes ÷ 60 = 100 hours/weekOver 6 months (~26 weeks): 100 × 26 = 2,600 hours total Assuming a full-time Data Manager works approximately 160 hours/month, over 6 months (960 hours) per full-time equivalent (FTE):2,600 ÷ 960 2.7 FTEs total for the entire study period To find the average per month, we divide evenly over 6 months:2.7 ÷ 6 0.45 FTE per month, or approximately 50% of a Data Manager per month. Thus, the correct answer is B. Fifty percent of a Data Manager per month. This estimate follows GCDMP best practices in resource planning, ensuring adequate data management capacity for ongoing external data handling activities.
(CCDM-Verified Sources)SCDM GCDMP, Chapter: Project Management, Section 5.3 Resource Estimation and Workload PlanningICH E6(R2) GCP, Section 5.1.1 Quality Systems and Adequate Staffing
Data from two sites are combined. One site coded gender as 1 and 2 (for Male and Female, respectively) while the other stored the data as M and F. Which term best describes the mapping?
Answer(s): D
When combining data from two datasets where one uses numeric codes (1 = Male, 2 = Female) and another uses text codes (M, F), each unique value in one dataset corresponds exactly to one unique value in the other.This relationship is a one-to-one mapping, where each element in one dataset maps directly to a single corresponding element in the other.1 M2 FSuch mappings ensure consistent data harmonization during data integration and standardization phases, as outlined in the GCDMP (Chapter: Database Design and Integration). Many-to-one (C) mapping would occur if multiple values (e.g., "Male," "M," "Man") mapped to a single standardized value, which isn't the case here.Thus, the mapping is one-to-one, ensuring precise correspondence between both representations of gender data.
(CCDM-Verified Sources)SCDM GCDMP, Chapter: Database Design and Build, Section 5.4 Data Mapping and Harmonization CDISC SDTM Implementation Guide, Section 5.2 Controlled Terminology and Mapping Rules ICH E6(R2) GCP, Section 5.5.3 Data Integrity and Integration Principles
All range and logic checks have been resolved in a study. An auditor found discrepancies between the database and the source. Which reason is most likely?
Even when all range and logic checks are successfully resolved, discrepancies may still exist between the clinical database and the source documents. This typically indicates an error in data abstraction or transcription, meaning that data were incorrectly entered or extracted from the source records during the data entry or verification process.According to the Good Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning), data validation rules such as range and logic checks are designed to identify inconsistencies, missing data, or out-of-range values within the database itself. However, they do not verify the accuracy of data entry against the original source documents -- that responsibility falls under source data verification (SDV), typically conducted by clinical monitors or auditors. When an auditor detects discrepancies between source and database values after all edit checks have passed, the most probable explanation is that data were not transcribed correctly from the source, rather than a failure in programmed edit checks. This could occur due to human error during manual data entry, misinterpretation of the source document, or oversight during SDV. Option C (Data were changed after checks were run) might occur in rare cases but would normally be documented in an audit trail per 21 CFR Part 11 and ICH E6 (R2) standards. Option B misinterprets the issue, since "logical and in range" values can still be incorrect relative to the source. Option A (Auditor error) is possible but statistically less likely, as source data verification follows strict, documented audit procedures.Therefore, the most likely reason for such discrepancies is Option D: Data were not abstracted correctly from the source, emphasizing the importance of robust data entry training, dual data entry, and verification procedures.
(CCDM-Verified Sources)Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.5 Source Data Verification and Reconciliation ICH E6 (R2) Good Clinical Practice, Section 5.18 Monitoring and Source Data Verification FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 Source Data Accuracy and Audit Trails21 CFR Part 11 Electronic Records and Electronic Signatures, Subpart B: Audit Trails and Record Accuracy
The serious adverse event (SAE) database should be reconciled against the clinical trial database prior to which occasion?
SAE reconciliation must be completed before database lock or closure to ensure all safety data are consistent between the clinical database and the pharmacovigilance (safety) database. According to the GCDMP (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 that no SAE is missing, misclassified, or inconsistently dated or coded between 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.
(CCDM-Verified Sources)SCDM Good Clinical Data Management Practices (GCDMP), Chapter: SAE Reconciliation, Section 6.1 Timing and Procedures for ReconciliationICH E2A/E2F Clinical Safety Data Management: Definitions and StandardsFDA Guidance for Industry: E2A Clinical Safety Data Management: Processing Standards for Safety Reports
A Data Manager is designing a CRF for a study for which the efficacy data are not covered by the current SDTM domains. Which of the following should the Data Manager consult first?
When efficacy data are not covered by existing CDISC SDTM domains, the first resource the Data Manager should consult is the CDISC Therapeutic Area Implementation Guide (TAIG) for that therapeutic field.According to the GCDMP (Chapter: Standards and Data Mapping), CDISC's Therapeutic Area User Guides (TAUGs) and Implementation Guides provide standardized data structures, variable definitions, controlled terminology, and implementation examples for specific diseases or therapeutic areas. These guides ensure consistency across studies, promote interoperability, and align data collection with regulatory submission expectations. Consulting other sponsors' forms or external registries (options A and C) can be informative but do not provide authoritative CDISC-compliant standards. SNOMED terms (option B) address medical terminology, not structural data domain definitions.Therefore, Option D is correct--CDISC TA Implementation Guides are the recognized primary reference when extending or designing SDTM-compliant CRFs.
(CCDM-Verified Sources)SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Standards and Data Mapping, Section 4.2 Use of CDISC StandardsCDISC Therapeutic Area User Guides (TAUGs) Implementation Guidance for Domain ExtensionFDA Data Standards Catalog CDISC Therapeutic Area Standards
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