Define the workflow
Map source data to explicit transformation specifications and SDTM/ADaM structures, including ADSL, ADAE and ADLB.
Regulated Data & AI Engineering
SDTM / ADaM Pipeline Automation
Metadata-driven clinical-data pipelines for biometrics, statistical programming and clinical data management teams.
Engineer the system
Integrate Pinnacle 21 checks, automated QC and reviewer workflows. Findings remain subject to documented assessment.
Review and operate
Connect SAS, R and Python processing to reproducible TLF generation and controlled downstream applications.
Agreed engineering deliverables
- Source-to-target mapping specifications and implemented pipeline
- QC outputs, review records and data-lineage documentation
- Reproducible reporting and validation-supporting evidence
Designed to support validation under the customer’s quality system. Validation responsibilities, acceptance criteria and human review are agreed explicitly; automation does not replace accountable quality review.
Technologies and standards chosen for the workflow
Statistical computing
R · Python · SAS · SQL
Clinical workflows
CDISC · SDTM · ADaM · Pinnacle 21 · TLF
Private AI
LLMs · RAG · on-premise · private cloud
Applications
Shiny · APIs · dashboards
Required inputs
Source schemas, representative approved data, metadata, applicable standards and current R/SAS interfaces.
Customer responsibilities
Provide authorized data access, clinical/statistical reviewers and quality-system requirements. The customer retains accountable quality review and formal validation responsibilities unless separately agreed.
Acceptance criteria
Agree mapping coverage, expected dataset structures, QC checks and review gates before implementation. Review exceptions, lineage and repeatability against the approved specifications.
Contact
Review your SDTM/ADaM pipeline
Metadata-driven clinical-data pipelines for biometrics, statistical programming and clinical data management teams.