CCDM Exam Dumps : Certified Clinical Data Manager

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

Let’s calculate the workload:

200 patients × 30 minutes = 6,000 minutes/week

6,000 minutes ÷ 60 =100 hours/week

Over 6 months (~26 weeks): 100 × 26 =2,600 hours total

Assuming a full-time Data Manager works approximately160 hours/month, over6 months (960 hours)per full-time equivalent (FTE):

2,600 ÷ 960 ≈2.7 FTEs total for the entire study period

To find theaverage per month, we divide evenly over 6 months:

2.7 ÷ 6 ≈0.45 FTE per month, or approximately50% of a Data Manager per month.

Thus, the correct answer isB. Fifty percent of a Data Manager per month.

This estimate follows GCDMP best practices inresource planning, ensuring adequate data management capacity for ongoing external data handling activities.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Project Management, Section 5.3 – Resource Estimation and Workload Planning

ICH E6(R2) GCP, Section 5.1.1 – Quality Systems and Adequate Staffing

In a relational clinical database, themost efficient and normalized structurefor data collected repeatedly over time—such asvital signs—isone record per patient per visit.

Each patient will have multiple records, one for each visit when vital signs are assessed. This structure supports:

Time-based analysis (e.g., trends across visits),

Accurate data linkage with visit-level metadata, and

Efficient querying for longitudinal data.

According to theGCDMP (Chapter: Database Design and Build), the relational design principle dictates that data should be stored at thelowest unique level of observation. Since vital signs vary by both patient and visit, the combination ofpatient ID + visit IDforms a unique key for each record.

Option A (per visit) lacks patient identification, while options B and D aggregate data too broadly, losing temporal detail.

Thus,option C (One record per patient per visit)correctly represents the normalized design structure.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 4.2 – Normalization and Table Structure

CDISC SDTM Implementation Guide, Section 5.3 – Visit-Level and Observation-Level Data Structures

ICH E6(R2) GCP, Section 5.5.3 – Data Handling Principles