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Term of the Day

Natural history study

A natural history study is a preplanned observational study intended to track the course of a disease over time, identifying demographic, genetic, environmental and other variables that correlate with its development and outcomes in the absence of intervention, or under standard of care. Designs may be retrospective (chart review of existing records) or prospective (longitudinal follow-up of a cohort or registry).

Natural history data is particularly important in rare and paediatric diseases, where randomised placebo-controlled trials may be infeasible or unethical. The FDA (guidance on rare disease natural history studies, 2019) and the EMA accept well-designed natural history studies to define endpoints and biomarkers, identify patient subgroups, estimate sample sizes and, in some cases, serve as external or historical control arms for single-arm trials supporting orphan products.

Because they are non-interventional, natural history studies fall outside the CTR and are governed by national law (for example France's MR-003 or MR-004 reference methodologies) and by the GDPR. They typically involve secondary use of medical records, long-term follow-up, genetic data and small populations in which anonymisation is rarely achievable, so pseudonymisation, a DPIA and a robust research legal basis under Art. 9(2)(j) are essential. Registries maintained by patient organisations or academic consortia raise additional questions of joint controllership and data access governance.

C

CDISC

CDISC, the Clinical Data Interchange Standards Consortium, is a global non-profit organisation that develops standards for clinical and non-clinical research data. Its standards give teams a shared structure for collecting, organising, analysing and describing study data, helping reviewers follow the evidence from collection to results.

Key takeaways

  • CDISC standards address different stages of the data lifecycle rather than one interchangeable format for every clinical research activity.
  • CDASH supports collection, SDTM supports tabulation, ADaM supports analysis, and Define-XML describes dataset metadata for regulatory reviewers.
  • FDA and PMDA submission requirements depend on the submission context and supported standard versions, not simply the CDISC label.

How the Main Standards Fit Together

StandardMain function
CDASHConsistent collection fields
SDTMTabulated study datasets
ADaMAnalysis datasets and traceability
Define-XMLDataset and variable metadata

CDISC identifies standards used in FDA and PMDA submissions. Operationally, agree the applicable versions, controlled terminology and validation expectations before building the study database, then check the receiving authority’s current requirements.

For example, consider a hypothetical trial collecting concomitant medications. Consistent questions at the site can support later tabulation, but the team still needs documented mappings and review of transformations. A standard does not remove the need for study-specific decisions.

Why It Matters for Data Protection

Standardised structure is not anonymisation. Operationally, review identifiers, free-text fields, access permissions and export packages separately from format compliance. A technically valid submission dataset can still contain more personal information than its intended recipient needs.

We recommend keeping a mapping record that shows both the scientific purpose of each field and the controls applied when data move between the sponsor, CRO and submission team.

Frequently Asked Questions

Is CDISC a regulator?

No. CDISC develops standards; authorities determine their regulatory use.

Is CDASH the same as SDTM?

No. Collection and tabulation are different stages, even when variables can be mapped.

Does using CDISC prove privacy compliance?

No. Data formats do not establish permission to collect, disclose or retain personal data.

Related: SDTM; ADaM; clinical-trial privacy support.

Updated: 27 October 2026