Clinical data is collected from multiple systems — EDC platforms, laboratories, safety databases, and external sources — each using different structures and standards. Before that data can support statistical analysis or regulatory submission, it must be standardized, organized, and fully traceable.
StatDoc specializes in transforming complex, multi-source study data into well-structured SDTM datasets that are compliant, transparent, and technically flawless.
Our approach blends deep CDISC expertise with a practical understanding of study design, ensuring that every dataset reflects the protocol, CRF, and real-world clinical flow.
Clean, validated, and compliant SDTM datasets built to current regulatory expectations.
Clear, fully documented data structures that streamline analysis, QC, and submission.
Consistent standards that reduce review cycles and eliminate avoidable queries.
Efficient workflows designed for future scalability and integration.
Transparent data lineage that withstands audit and regulatory scrutiny.
StatDoc ensures that SDTM programming is not just compliant but insightful — delivering structured, traceable datasets that form a reliable foundation for analysis, reporting, and global submission.
Develop detailed mapping specifications, annotated CRFs, and variable derivation notes. Provide reviewer-friendly documentation that clearly explains transformation logic and data relationships. Maintain metadata repositories for reuse and standardization across studies
Perform automated and independent QC reviews at both variable and domain levels. Validate datasets against FDA, EMA, and PMDA technical conformance guidelines. Verify domain structure, variable attributes, controlled terminology, and cross-domain integrity. Reconcile subject counts, visit ordering, and date/time consistency prior to delivery.
Maintain current versions of CDISC, MedDRA, and WHODrug dictionaries. Apply consistent coding conventions to ensure interoperability and data pooling capability. Harmonize terminology across therapeutic areas for program-level consistency.
Establish full traceability from source data to SDTM and onward to ADaM and reporting outputs. Maintain derivation logic and mapping matrices to support audits and submission review. Ensure every variable can be traced back to its original origin for complete accountability.
Create study-level SDTM templates designed for reuse across future projects. Build modular, version-controlled frameworks that reduce programming time and increase consistency. Support long-term scalability for integrated summaries and cross-study analyses.