Clinical Data Quality Checks for CDISC Compliance Using SAS

Clinical-trial datasets can look orderly and still conceal problems: a missing required value, a duplicate record, an unexpected date sequence, or a mismatch between data and specifications. In Clinical Data Quality Checks for CDISC Compliance Using SAS, Sunil Gupta sets out practical ways to detect and monitor these issues across SDTM and ADaM data.

The focus is on building repeatable checks rather than relying on a last-minute manual review. SAS programming examples—including PROC SQL, metadata, and macro techniques—show how checks can be organized and adapted as data and project requirements change.

From dataset basics to meaningful checks

The book begins with an overview of data-quality and compliance checks: what levels to examine, when checks are useful, and how to account for the needs of different project teams. It then addresses dataset “vitals,” data transfers, descriptive statistics, empty datasets, and selected population and oncology considerations.

Find the issues that affect interpretation

Dedicated sections examine practical data problems such as duplicate records, missing required variables, date and time formats, durations, out-of-range or negative values, numeric outliers, and relationships between variables. Derived values and laboratory data receive attention too, helping readers think beyond isolated field-level checks.

Keep data aligned with CDISC specifications

Later chapters turn to specification and data-compliance checks, including dataset attributes and variable order, SDTM and ADaM consistency, protocol-related checks, and codelist dictionary compliance. The chapter-by-chapter progression makes it easier to connect an individual check to the broader process of reviewing clinical data.

Make recurring review work more manageable

Gupta also describes programs for assembling unique values across SDTM and ADaM variables and comparing codelist dictionaries with specifications. These examples demonstrate how automation can make recurring checks more systematic and help teams spot differences that might otherwise take time to find manually.

For clinical SAS and data teams

This book is especially relevant to SAS programmers who work with clinical-trial data and CDISC standards. Data managers, statisticians, and other team members involved in data review or submission preparation may also find its organized approach to checks useful. For readers looking to build a more consistent framework for data-quality review, Gupta offers a focused, code-oriented place to begin.