AI Agentic Workflow for Automated Transformation of Clinical Data into Analysis Data Model (ADaM) Datasets

Manual creation of ADaM datasets is a sequential, programmer-intensive process involving mapping logic, derivation, and validation that can take weeks per study. A custom agentic workflow automates these repetitive tasks—executing CDISC rules, generating derived variables (e.g., AVAL, CHG), and producing define.xml—freeing senior programmers for complex analysis. The result is dataset delivery in days, not weeks, directly accelerating integrated summary timelines.

Submission deadlines force teams to scale statistical programming capacity with expensive contractors, creating budget volatility and onboarding overhead. Automating the foundational ADaM build with a custom workflow creates a predictable, scalable core capacity. This reduces reliance on contingent labor for routine dataset creation, allowing fixed FTEs to manage more studies and reallocating budget to higher-value statistical innovation and submission strategy.

Manual programming introduces inconsistencies and errors that surface as major deficiencies during agency review, leading to costly information requests. An automated workflow embeds validation checkpoints directly into the data pipeline. Agents perform cross-dataset consistency checks, validate against CDISC standards, and flag outliers before datasets are locked. This proactive quality layer reduces rework and strengthens the defensibility of the submission package.

Waiting for final ADaM datasets creates a critical path delay for medical writers drafting clinical summaries and for statisticians performing integrated analyses. An automated workflow decouples dataset creation from manual effort, enabling parallel workstreams. Draft datasets and metadata are available earlier for review. For amendments, the system can rapidly re-execute logic with new data cuts, turning a multi-week update into a matter of days.

Ad-hoc programming scripts and tribal knowledge create operational risk and make processes difficult to scale or inspect. A custom workflow codifies ADaM generation logic into a version-controlled, observable orchestration layer (e.g., using LangGraph). Every derivation, decision, and validation is logged, creating a complete audit trail for internal QA and regulatory inspection. This transforms a bespoke art into a repeatable, governable production system.

By automating the commoditized work of dataset construction, your top statistical talent is freed from syntax and mapping to focus on complex modeling, exploratory analyses, and innovative endpoint development. This shifts the function's role from a cost center executing specifications to a strategic accelerator that explores deeper insights, strengthens the evidence package, and improves the probability of regulatory and commercial success.

This core agent applies sponsor-specific statistical analysis plans (SAPs) and CDISC ADaM Implementation Guides to transform SDTM domains. It executes derivation logic for endpoints (e.g., change from baseline, responder analysis) and creates variables like AVAL, CHG, PARAM, PARAMCD. The agent operates within a version-controlled logic repository (e.g., Git) and logs all transformations for an immutable audit trail, directly replacing programmer scripting in SAS or R.

A dedicated agent automatically generates the define.xml metadata file required for submission. It ingests the newly created ADaM datasets, maps variables to their SDTM origins, and populates controlled terminology. The agent structures the XML according to the CDISC Define-XML standard, ensuring technical compliance and eliminating the error-prone, manual documentation process that typically follows dataset creation.

This agent runs a suite of pre-programmed validation checks against each new ADaM dataset. It verifies traceability to SDTM, checks for missing values in key analysis variables, validates derivation logic, and ensures ADaM Basic Data Structure (BDS) compliance. Failed checks are routed to a human-in-the-loop queue for review and exception handling, creating a governed quality gate before datasets are released for analysis.

The central orchestrator (built with LangGraph or Apache Airflow) manages the workflow's state, triggers agents in sequence, and handles retries. It connects directly to the clinical data warehouse (e.g., Informatica, Snowflake) to pull source SDTM data and write final ADaM datasets back. This component manages dependencies, such as waiting for all SDTM domains to be locked before initiating ADaM creation, and integrates with RIM systems (e.g., Veeva Vault) to update dataset status.

A critical control layer providing a web-based review interface for statistical programmers. It surfaces agent outputs, validation flags, and derivation logs. Programmers can approve datasets, reject them with comments back to the agent queue, or manually override specific derivations. All actions are logged for compliance, ensuring human oversight is embedded efficiently without becoming a bottleneck.

The final agent in the chain packages and routes the validated ADaM datasets and define.xml to downstream consumers. It can push datasets to statistical computing environments (e.g., SAS Grid, RStudio Connect) for table/figure generation, and to submission publishing tools (e.g., Liquent Publisher) for inclusion in the eCTD. This automates the last-mile delivery, ensuring analysis-ready data is instantly available for CSR and ISS/ISE compilation.

The workflow's primary integration point is the SDTM-compliant data warehouse (e.g., SAS datasets, RDF triplestores). Agents must execute within a secure, governed compute environment with direct access to raw and intermediate datasets. The architecture must support idempotent processing runs and maintain a clear audit trail of all transformations applied, linking back to the source SDTM variables and the statistical algorithms used. Failure to design for traceability here creates significant rework during regulatory queries.

Effective automation requires decomposing the ADaM build into specialized agents. A Mapping Agent interprets CDISC Controlled Terminology and Analysis Results Metadata (ARM) to define derivations. A Derivation Agent executes validated SAS or R macros for complex calculations (e.g., time-to-event, imputation). A Metadata Agent auto-generates define.xml and reviewer's guides. This separation allows for targeted validation, easier debugging, and the ability to update logic for one derivation without retesting the entire pipeline.

Automation does not eliminate review; it repositions it. The workflow must include automated validation checks against the ADaM Implementation Guide (e.g., variable naming, label consistency, presence of required datasets). Outputs then route to a Biostatistician Review Queue for sign-off on derived variable logic and a Data Manager Queue for final quality control. The system must support exception routing with full context (input data, code, agent reasoning) to prevent bottlenecks. This controlled handoff is critical for regulatory acceptance.

The agent network requires a robust orchestration layer, typically built on LangGraph or Apache Airflow, to manage state, dependencies, and retries. Deployment must align with IT governance: will agents run in a containerized cloud environment (AWS/GCP/Azure) or an on-premises SAS Grid? The choice impacts data egress costs, latency, and integration with existing electronic submission publishing tools. A phased pilot, starting with a single study or a common derivation like BASE, de-risks the operational rollout.

The core ROI is measured in statistical programmer weeks recovered. Automating routine derivations (e.g., ADSL, ADTTE, ADAE) can compress dataset creation from 4-6 weeks to 5-7 days. This directly accelerates Integrated Summary of Efficacy (ISE) and Clinical Study Report (CSR) timelines. The economic value isn't just labor savings; it's reduced submission cycle risk and the ability to reallocate high-cost programming talent to novel, complex analyses that truly require expert intervention.

A static workflow fails when the study protocol amends. The system must have a version-controlled rule repository where changes to derivation logic (triggered by a protocol amendment) are reviewed and approved before being deployed. Furthermore, any LLM-assisted components (e.g., for interpreting analysis plans) require continuous monitoring for logic drift. A governance council (Stats, Programming, Data Management) must own the change control process to ensure the automated output remains statistically defensible and protocol-compliant.