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Clinical Trial Automation

What is clinical trial automation?

A plain-language guide to how software and AI turn a protocol and raw study data into submission-ready evidence — what gets automated, where experts stay in control, and how it differs from a CTMS or EDC.

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What is clinical trial automation?

Clinical trial automation is the use of software and AI to execute the data, statistical, and regulatory work of a clinical trial — the steps that turn a protocol and raw study data into submission-ready evidence — with less manual programming and fewer hand-offs. In practice it means automating the statistical analysis plan (SAP), eCRFs, SDTM and ADaM datasets, tables, listings and figures (TLFs), Define-XML, quality control, and FDA submission documents, while qualified biostatisticians and programmers review and approve each output. Astraea is clinical trial automation software that pharmaceutical sponsors run themselves, inside their own environment — not a CRO, and not a web-hosted service — so the trial accelerates without the data ever leaving the sponsor's control.

What Gets Automated

The trial lifecycle, stage by stage.

Clinical trial automation concentrates on the work after protocol design and raw data collection — the biometrics and submission layer where standards, traceability, and speed all matter at once.

Statistical Analysis Plan (SAP)

Structure and draft the SAP from the protocol so downstream programming has a machine-readable specification to build against.

eCRF & aCRF Design

Generate CDISC-annotated case report forms from protocol artifacts and metadata, keeping data collection standards-native from the start.

SDTM Mapping

Map collected study data into SDTM-conformant domains with controlled terminology and full lineage, proposed by AI and confirmed by your team.

ADaM Datasets

Derive analysis-ready ADaM datasets from SDTM inputs and the SAP, preserving traceability from every analysis value back to its source.

TLF Generation

Program tables, listings, and figures directly against the SAP, compressing the biometrics-to-reporting window while keeping outputs reviewable.

Define-XML & eCRT

Produce the submission metadata and documentation that travel with datasets, so packages arrive standards-conformant rather than manually assembled.

Quality Control & Validation

Run conformance and edit checks against CDISC rules automatically, flagging issues for human adjudication before anything advances.

CSR Draft Generation

Draft clinical study report narratives and results sections from the SAP, datasets, and TLFs, keeping medical writers in control of the final document.

Submission Documents

Assemble the regulator-ready artifacts of an FDA submission with audit-ready traceability intact at every step.

Common Questions

Clinical trial automation, answered.

The definitional questions sponsors, biostatisticians, and clinical data managers ask when they start evaluating automation.

Clinical Trial Automation — The Basics

What is clinical trial automation?
Clinical trial automation is the use of software and AI to execute the data, statistical, and regulatory work of a trial — turning a protocol and raw study data into submission-ready evidence with less manual programming and fewer hand-offs. It typically covers the SAP, eCRFs, SDTM and ADaM datasets, TLFs, Define-XML, quality control, and FDA submission documents, with human experts reviewing and approving each output.
What parts of a clinical trial can be automated?
The most automatable work sits after protocol design and raw data collection: statistical analysis plan structuring, eCRF/aCRF design, SDTM mapping, ADaM dataset derivation, tables-listings-figures (TLF) generation, Define-XML and eCRT metadata, QC and conformance validation, and the assembly of submission documents. Judgment-heavy decisions stay with biostatisticians and programmers; the software does the heavy, repetitive execution.
How is clinical trial automation different from a CTMS or EDC?
A CTMS manages trial operations (sites, budgets, milestones) and an EDC captures trial data. Clinical trial automation focuses on the biometrics and submission layer — transforming captured data into standards-conformant datasets, analyses, and regulatory documents. The categories are complementary: EDC and CTMS run the study; automation software turns its data into a submission.

Benefits, Compliance & Data Security

What are the benefits of automating clinical trial biometrics?
Automation compresses the biometrics-to-reporting window, reduces manual programming errors, and keeps outputs standards-native (CDISC SDTM/ADaM, Define-XML) so submissions are ready sooner. Because every derivation is versioned and traceable, teams also gain reproducibility and audit-readiness that manual, spreadsheet-driven pipelines struggle to maintain.
Is clinical trial automation compliant with FDA requirements?
It can be, when the software is built around the relevant controls. Astraea is designed to support 21 CFR Part 11 (audit trails, access controls, validation, linked electronic signatures), ALCOA+ data integrity, and CDISC standards, following FDA's risk-based, human-centric posture toward AI in clinical development. Qualified people retain sign-off and regulatory accountability for every output.
Does automation software need access to our patient data?
Not with an in-environment model. Astraea is installed and operated inside the sponsor's own environment by forward-deployed engineers, so proprietary study data and PHI stay within the sponsor's security boundary. Astraea never sees or holds your data, and your data is never used to train shared or third-party models — a core security difference versus cloud-hosted platforms.

See clinical trial automation in practice.

Book a working session with our team — we'll walk through how Astraea automates your SAP, standards, and submission workflow inside your own environment.

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