SCDM CCDM Exam - Topic 4 Question 8 Discussion

Actual exam question for SCDM's CCDM exam

Question #: 8
Topic #: 4

[All CCDM Questions]

Which metrics report listed below would best help identify trends in the clinical data?

APercent of data/visits cleaned

BLast patient/last visit date to data lock date

CNumber of subjects screened/enrolled

DQuery frequency counts per data element

Show Suggested Answer

Suggested Answer: D

The Query frequency counts per data element (Option D) is the best metric for identifying data trends and potential systemic data issues in clinical trials.

According to the Good Clinical Data Management Practices (GCDMP, Chapter: Data Quality Assurance and Control), trend analysis involves identifying recurring data issues across subjects, sites, or variables to detect training gaps, protocol misinterpretation, or CRF design flaws. A high number of queries generated for specific fields (e.g., visit date, lab values, or dosing information) may indicate systemic problems such as unclear CRF instructions or site-level misunderstandings.

While metrics such as percent of data cleaned (A) and time to database lock (B) reflect overall progress and efficiency, they do not identify specific data pattern issues. The number of subjects screened/enrolled (C) pertains to recruitment rather than data quality.

Therefore, query frequency per data element provides actionable insights for quality improvement, process refinement, and early identification of potential risks.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 6.3 -- Metrics and Trend Analysis

ICH E6 (R2) Good Clinical Practice, Section 5.18.4 -- Risk-Based Quality Review and Data Trends

FDA Guidance for Industry: Oversight of Clinical Investigations -- Risk-Based Monitoring, Section 6 -- Data Metrics and Trend Evaluation

by Truman at Nov 22, 2025, 11:51 PM

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Justine

9 hours ago

B seems like a good indicator of timelines.

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Leanora

6 days ago

Query frequency counts per data element? Sounds like a job for a data detective!

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Stefany

11 days ago

Haha, "data/visits cleaned" - sounds like my apartment after a wild party!

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Aileen

16 days ago

Percent of data/visits cleaned? Really? That's just basic data hygiene, not a trend indicator.

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Kayleigh

21 days ago

Last patient/last visit date to data lock date seems like the most relevant metric to me.

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Pamela

26 days ago

I think the number of subjects screened/enrolled would be the best metric to track. That's the heart of the study.

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Rasheeda

1 month ago

I lean towards option C because knowing how many subjects were screened could indicate recruitment trends, but I'm not entirely confident.

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Regenia

1 month ago

I'm not really sure, but I remember something about the last patient/last visit date being important for timelines.

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Erin

1 month ago

I think option D might be the best choice since query frequency counts can highlight data issues over time.

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Dottie

2 months ago

I'm leaning towards the number of subjects screened/enrolled. That feels like the most direct way to see how the study is going in terms of participant recruitment and retention.

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Lorenza

2 months ago

The percent of data/visits cleaned seems like it could be a helpful metric too. Making sure the data is clean and accurate is crucial for identifying meaningful trends.

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Rosalind

2 months ago

I'd go with the last patient/last visit date to data lock date. That could give us a good sense of how the data is progressing and whether there are any delays in the study timeline.

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Josephine

2 months ago

A is super important for data quality!

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Jonell

2 months ago

I think C is key for tracking enrollment trends.

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Maybelle

2 months ago

The query frequency counts per data element would definitely help identify trends in the clinical data.

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Sherly

3 months ago

I practiced a similar question, and I feel like the percent of data cleaned could show trends too, but maybe not as effectively as the others.

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Viola

3 months ago

Overall, D seems to provide the most actionable insights.

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Vi

3 months ago

I'm a bit unsure about this one. The query frequency counts per data element could also be useful to spot any issues or inconsistencies in the data, but I'm not sure if that's the best option for identifying overall trends.

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Sabrina

3 months ago

Hmm, I think the number of subjects screened/enrolled would be a good metric to look at. That could help identify trends in patient recruitment over time.

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