The protocol requirement has two key components: the value and the temporal condition (within 90 days). To ensure both are met and the data is auditable, the EDC must capture the test date as discrete data. An edit check validates the protocol requirement in real-time, improving data quality and reducing queries. Relying on source or comments does not provide discrete, machine-readable data for analysis or easy verification.

Using numerous local labs introduces major data challenges: varying units of measure, reference ranges, and analytical methods. This can render data non-comparable and unusable for analysis. A CCDM must highlight this risk and advocate for a proactive plan that includes collecting normal ranges, units, and potentially post-hoc normalization, ensuring data from all sources can be integrated meaningfully. Ignoring the issue or using free-text compromises data integrity and analysis readiness.

Accurate safety analysis depends on consistent terminology and structured data. Auto-coding from a verbatim term ensures standardization for grouping and analysis (e.g., calculating incidence). Capturing associated details (severity, dates, seriousness) as discrete data points is essential for understanding the event’s profile, frequency, and relationship to study drug. A single text field or broad categories lose critical structure and hinder analysis.

The primary value of dictionary coding is data standardization. Free-text entries lead to countless variations (brand names, misspellings, abbreviations) that make analysis nearly impossible. Coding to a standard dictionary allows the study team to reliably search, group, and analyze medications by ingredient or class across the entire dataset, which is fundamental for safety and efficacy analyses related to drug interactions.

Edit checks that fire in real-time (or near real-time) within the EDC provide immediate feedback to the site. For critical eligibility criteria, this is the most effective control point. It prevents protocol deviations (like randomizing an ineligible subject) and enhances patient safety by flagging potentially concerning values when they are entered, allowing for immediate follow-up. Post-hoc checks are valuable but are detective, not preventive.

Protocol amendments are prospectively implemented. Data collected correctly under a prior protocol version is valid historical data and must not be altered or deleted, as this would be falsification. The correct approach is to document the change, update the EDC and data management plan for future data collection, and ensure the analysis plan accounts for the two different collection timepoints. This maintains data integrity and audit trail.

Blind data review is a quality control measure specific to blinded trials. Its goal is to protect the blinding integrity. Reviewers who are themselves blinded look for patterns in data (e.g., lab values, AE profiles) that might reveal the treatment group. If such patterns are found, measures can be taken to mask them in reports seen by blinded team members. It is not about source data verification or early efficacy analysis.

SDTM is a submission standard mandated by regulators like the FDA. It provides a consistent, predefined structure for organizing data (e.g., Demographics, Adverse Events, Laboratory Results) into datasets with standardized variables. This standardization allows regulatory reviewers to efficiently navigate, analyze, and compare data across different submissions. It is not a data collection model but a transformation and tabulation model for submission.

Vendor data is a critical component of the study database (e.g., treatment assignment, randomization). Formal reconciliation is a mandatory Good Clinical Practice (GCP) control. It involves comparing datasets from different systems using a predefined list of key variables to ensure they tell the same story. Discrepancies must be identified and resolved before database lock to ensure the final integrated database is accurate and consistent.

The protocol requirement has two key components: the value and the temporal condition (within 90 days). To ensure both are met and the data is auditable, the EDC must capture the test date as discrete data. An edit check validates the protocol requirement in real-time, improving data quality and reducing queries. Relying on source or comments does not provide discrete, machine-readable data for analysis or easy verification.0