Clinical data management, CDISC (SDTM & ADaM) automation, RBQM analytics, and multi-EDC harmonization from a specialized clinical trial data analytics partner. We ship submission-ready clinical data pipelines.
TRUSTED BY CLINICAL DATA LEADERS AT PHARMA, BIOTECH & SPECIALTY CROS
Every symptom below traces back to the same disease — a fractured clinical data pipeline that stalls CDISC deliverables and pushes database lock out of reach.
Rave here, Veeva there, Oracle Clinical for legacy studies. Without multi-EDC harmonization, every downstream SDTM ADaM deliverable is rebuilt from scratch.
SAS programmers hand-map every domain every study. Automated SDTM mapping enforces CDISC compliance and cuts mapping weeks to days.
Findings surface late, force resubmission cycles, and delay database lock clinical trial timelines. Continuous validation kills the loop.
SDV-heavy monitoring misses the risks that matter. ICH E6(R3)-aligned RBQM analytics with KRIs focuses oversight where it moves the needle.
Ops leads chase status in email threads. A unified clinical trial dashboard delivers real-time clinical operations analytics on enrollment, queries, and KRIs.
Central lab, biomarker, and ePRO feeds arrive late or unmapped. Clinical laboratory data analytics integrated into the pipeline closes the gap.
Sponsors and CROs run on fragmented EDCs, manual SDTM mapping, and reactive monitoring. Our clinical data analytics practice replaces that with an automated, CDISC-compliant clinical data pipeline that shortens time to database lock.
Connect with our clinical trial data analytics team. We'll skip the sales pitch and dig into your specific CDISC, RBQM, and multi-EDC bottlenecks.
Most clinical data management programs miss database lock targets not because sites are slow — but because the clinical data pipeline was never engineered for CDISC compliance at speed.
Medidata Rave for one study, Veeva Vault CDMS for another, Oracle Clinical for legacy programs. Without multi-EDC harmonization, every downstream clinical trial data analytics deliverable — SDTM, ADaM, RBQM analytics, clinical trial dashboard — has to be rebuilt from scratch per study.
SAS programmers hand-map every SDTM domain, every study, every amendment. That is where CDISC compliance drift enters — and it is the single biggest reason data analytics for clinical trials teams miss lock dates.
Validating SDTM ADaM only at the end of the study forces rework loops that push database lock clinical trial timelines out by weeks. Continuous validation inside the clinical data pipeline surfaces issues at ingestion instead.
Both are CDISC standards, both are required for submission, but they serve very different jobs in the clinical data pipeline.
| Dimension | SDTM | ADaM |
|---|---|---|
| Purpose | Standardize collected trial data | Enable statistical analysis and traceability |
| Structure | Domain-based (DM, AE, LB, VS, EX, etc.) | Analysis datasets (ADSL, ADAE, ADLB, ADTTE) |
| Timing in Pipeline | Immediately after EDC / lab ingestion | Downstream of SDTM, before TFLs |
| FDA Requirement | Required for submission of collected data | Required for submission of analysis data |
| Key Metadata | SDTM Implementation Guide, Define.xml | ADaM IG, ADaM Define.xml, analysis metadata |
| Who Owns It | Clinical data management / data engineering | Biostatistics and statistical programming |
Clinical Data Interchange Standards Consortium — the global body whose SDTM, ADaM, and Define.xml specifications govern clinical trial data submissions worldwide.
Study Data Tabulation Model — the CDISC standard that organizes collected clinical trial data into standardized domains required by FDA and PMDA reviewers.
Analysis Data Model — CDISC analysis-ready datasets derived from SDTM, engineered so every analysis value traces back to its source.
The machine-readable metadata document describing SDTM and ADaM datasets, variables, controlled terminology, and derivations for a submission package.
Risk-Based Quality Management — the ICH E6(R3)-aligned model using KRIs and centralized statistical monitoring to focus oversight where risk is highest.
The formal freeze of a clinical trial database after cleaning, query resolution, and CDISC validation are complete — the gate before statistical analysis.
Revised ICH Good Clinical Practice guideline formalizing risk-based, quality-by-design trial conduct and expecting proportionate RBQM analytics oversight.
A trial or program capturing data across more than one EDC (Medidata Rave, Veeva Vault CDMS, Oracle Clinical), requiring harmonization into one clinical data pipeline.
Not every vendor claiming clinical data analytics chops can actually ship a CDISC-compliant clinical data pipeline. Here is what separates a true partner.
| Criterion | Internal SAS Team | Generic CRO | Perceptive Analytics |
|---|---|---|---|
| CDISC Automation | Hand-coded per study | Templated SAS macros | Automated SDTM/ADaM + continuous Pinnacle 21 |
| Submission Speed | Weeks of rework | Standard CRO SLA | Days to submission-ready SDTM ADaM |
| Multi-EDC Support | Single EDC only | Preferred EDC only | Multi-EDC harmonization across Rave / Veeva / Oracle |
| RBQM Capability | Ad-hoc KRIs | Vendor-locked RBQM tool | ICH E6(R3)-aligned RBQM analytics |
| Cost Model | Full FTE overhead | Bundled functional service | Flexible pod, scale up/down per study |
| Clinical Trial Dashboard | Excel status decks | Static PDF reports | Live dashboard driving clinical operations analytics |
