Clinical Data Management CCDM Reddit Questions

In aSQL (Structured Query Language)database, theSELECTstatement specifies which columns to display from a table. In this query, only three columns —Pt_ID,MRN, andSSN— are being selected from thepatienttable.

This means the resulting dataset will contain:

The same number ofrows (records)as the original table (assuming noWHEREfilter), and

Fewer columnsthan the full table.

In database terminology:

“Wider” refers to more columns (fields).

“Narrower” refers to fewer columns (fields).

Since this query retrieves only 3 columns (out of potentially many in the original table), the result set isnarrower than the patient table, makingoption Dcorrect.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 5.1 – Relational Databases and Query Logic

ICH E6(R2) GCP, Section 5.5.3 – Data Retrieval and Integrity Principles

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.4 – Database Query Controls

Afront-end checkis a type ofreal-time validationperformed at the point of data entry—typically within anElectronic Data Capture (EDC)system or data entry interface—designed to ensure that the data entered in a text box (or any input field) isvalid, logically correct, and within expected parametersbefore the user can proceed or save the record.

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning),edit checksare essential components of data validation that ensure data accuracy, consistency, and completeness. Front-end checks are implemented within the data collection interface and are triggered immediately when data are entered. They prevent invalid entries (such as letters in numeric fields, out-of-range values, or improper date formats) from being accepted by the system.

Examples of front-end checks include:

Ensuring a numeric field accepts only numbers (e.g., weight cannot include text characters).

Validating that a date is within an allowable range (e.g., not before the subject’s date of birth).

Requiring mandatory fields to be completed before moving forward.

This differs fromback-end checksorprogrammed checks, which are typically run later in batch processes to identify data inconsistencies after entry.Manual checksare human-performed reviews, often for context or data that cannot be validated automatically (e.g., narrative assessments).

Front-end edit checks are preferred wherever possible because theyprevent errors at the source, reducing the number of downstream data queries and cleaning cycles. They contribute significantly todata quality assurance,regulatory compliance, andefficiency in data management operations.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.2 – Edit Checks and Real-Time Data Validation

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – Data Entry and Verification Controls

ICH E6 (R2) Good Clinical Practice, Section 5.5 – Data Handling and Record Integrity

CDISC Operational Data Model (ODM) Specification – Edit Check Implementation Standards

RACIis aproject management and governance frameworkused to defineroles and responsibilitieswithin a project. Each letter represents a distinct role type:

Responsible (R):The person(s) who perform the work or execute the task.

Accountable (A):The individual ultimately answerable for the task’s completion and success (only one per activity).

Consulted (C):Subject matter experts who provide input or guidance before decisions are made.

Informed (I):Individuals kept up to date on progress or outcomes but not directly involved in execution.

The RACI model ensuresclarity in ownership and accountability, preventing duplication of effort or responsibility confusion. It is a key component of theGCDMP (Chapter: Project Management in Data Management)for ensuring clear delegation and communication within clinical data management teams.

Hence,option Dis correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Project Management in Data Management, Section 5.1 – Roles, Responsibilities, and RACI Matrices

Project Management Institute (PMI) Framework – Responsibility Assignment Matrices (RACI)

ICH E6(R2) GCP, Section 5.1.1 – Defined Roles and Quality Oversight Responsibilities

To identifysite variability in eligibility screening, the most effective approach is toplot eligibility rate by site. This allows visual detection of differences in how well each site screens subjects according to protocol-defined inclusion and exclusion criteria.

TheGCDMP (Chapter: Data Quality Assurance and Metrics)emphasizes the importance of graphical analysis for identifying anomalies and site-level performance variability. By plotting the eligibility rate by site, data managers and clinical operations teams can quickly identify outliers—sites that screen too many or too few eligible subjects—indicating potential training issues, misunderstanding of inclusion/exclusion criteria, or even possible protocol deviations.

While summarizing screening rate (B) provides useful numeric data, it lacks visual comparability. Listing waivers (A) or enrollment counts (C) provide limited insights intoeligibility consistency.

Therefore,option D—Plot eligibility rate by site—is the best analytic and visualization practice to assess site variability in screening outcomes.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Control, Section 6.1 – Use of Metrics and Graphical Review for Site Performance

ICH E6(R2) GCP, Section 5.18.4 – Identification of Systematic or Site-Specific Issues