From Raw Study Data to Regulatory Submission
Regulatory agencies require study data to conform to defined standards — SDTM and ADaM for clinical trials, SEND for nonclinical studies — before submissions can be accepted. Meeting these requirements demands not only data transformation and mapping, but rigorous validation, traceability, and version control across often complex, multi-study programmes.
Tools in this domain address two tightly linked challenges. The first is the organisation and standardisation of study data itself: building compliant datasets, maintaining controlled terminology, managing define.xml and submission packages, and ensuring datasets pass regulatory validation checks. The second is making that standardised data analytically useful — producing tables, figures, and listings, conducting cross-study analyses, and supporting the statistical computing environments where programmers and biostatisticians operate.
These capabilities are used across clinical development and nonclinical safety teams, typically spanning data management, statistical programming, and regulatory affairs functions. The degree of integration between standards management and downstream analytics is increasingly a defining factor in how efficiently organisations can move from study data to submission-ready outputs.
CDISC Compliance Software by Specialisation
Platforms that centralise and standardise clinical and nonclinical study data through SDTM, ADaM, and SEND repositories, with automated packaging for regulatory submissions.
Software for interactive visualisation, cross-study analytics, TFL generation, and statistical computing across integrated clinical and nonclinical datasets.
CDISC Compliance Software: Common Challenges
- Inconsistent data mapping across studies
Datasets mapped independently across studies accumulate terminology inconsistencies that create downstream validation failures and delay submissions.
- Manual CDISC validation is error-prone
Manually checking compliance against Pinnacle 21 or FDA validation rules introduces risk and consumes significant statistical programming resource.
- Submission package assembly is fragmented
Define.xml, reviewer's guides, and dataset metadata are managed across disconnected tools, making submission packaging slow and audit-prone.
- Cross-study analysis lacks a unified dataset
Pooled analyses and integrated summaries are difficult when SDTM or ADaM datasets are stored and versioned inconsistently across trials.
- TFL production is disconnected from source data
Tables, figures, and listings generated outside a governed pipeline are hard to trace back to validated, submission-ready datasets.
- SEND compliance for nonclinical data is underdeveloped
Nonclinical teams often lack dedicated tooling to convert legacy study data into SEND-compliant datasets required for regulatory submissions.
CDISC Compliance Software Use Cases
- Preparing an NDA or MAA data package
Statistical programmers use standardised repositories and submission packaging tools to assemble complete, validation-passing data packages for agency review.
- Mapping raw clinical data to SDTM
Data managers work through domain-by-domain mapping against CDASH or legacy CRF structures ahead of first statistical analysis.
- Running integrated summaries of safety
Biostatisticians pool SDTM or ADaM datasets across multiple trials to produce the integrated safety summaries required for submission dossiers.
- Validating nonclinical SEND datasets
Nonclinical data managers convert toxicology study data into SEND format and run validation checks prior to IND or NDA filing.
- Producing submission-ready TFLs at scale
Statistical programming teams generate and quality-check large volumes of tables, figures, and listings against a governed, traceable data pipeline.
- Supporting regulatory agency data reviewers
Submission teams structure define.xml and dataset metadata so agency reviewers can navigate and interrogate clinical data efficiently during review.
Evaluating CDISC Compliance Software: Key Questions
- Which CDISC standards versions and controlled terminology releases does the platform currently support?
- How does the tool handle define.xml generation and version control across submission packages?
- Can validation rules be customised to align with specific agency or study programme requirements?
- How are ADaM derivations and traceability from SDTM documented and maintained within the platform?
- Does the platform support both clinical SDTM/ADaM and nonclinical SEND workflows within the same environment?
Is CDISC Compliance Software Right for Your Team?
- Your team prepares or contributes to clinical or nonclinical data packages for regulatory submissions to FDA, EMA, or PMDA.
- You manage SDTM, ADaM, or SEND dataset production and need governed, validated workflows rather than ad hoc scripting environments.
- Your organisation runs multiple trials simultaneously and requires consistent standards application and cross-study dataset pooling.
- You are responsible for statistical programming, TFL delivery, or submission-ready define.xml and metadata documentation.
- Your nonclinical data is subject to SEND requirements and your current process for conversion and validation is manual or fragmented.
Example Tools On Our Platform
Mapper
- Data mapping and standardization from any source without programming, using drag-and-drop transformation to SDTM and other clinical data standards.
Spotfire
- Visual analytics and data discovery for scientific R&D and clinical development, combining visualization, data wrangling, predictive analytics, and collaboration.
Xact Software Suite (StatXact, LogXact, Xact PROCs)
- Exact statistical analyses for small, sparse, and missing data in clinical research.
BRAIN
- Centralized statistical programming and biostatistics workflows for generating submission-ready tables, listings, and figures with full audit traceability.
Biostatistics & Programming
- SDTM/ADaM dataset generation and protocol-to-TLF automation with Agentic AI for clinical biostatistics workflows.
ClinEvra
- Transform clinical protocols into structured, submission-ready metadata and assets with AI-powered extraction and governed workflows.
Related Life Science Software
- Clinical Trial Management
Trial data collected and managed in CTMS systems feeds directly into CDISC standardisation and submission preparation workflows.
- Regulatory & Safety Documentation
Standardised clinical datasets underpin the safety narratives and regulatory documents assembled for submission dossiers.
- Clinical & Health Data Management
Source clinical data governance and EDC outputs are upstream dependencies for SDTM mapping and standards compliance.
- Computational Drug Safety & PKPD Modeling
ADaM datasets and integrated summaries feed PK/PD and safety modelling workflows that inform regulatory benefit-risk assessments.
- Scientific Informatics & Analytical Platforms
Statistical computing environments used for ADaM programming and TFL production often intersect with broader scientific informatics infrastructure.