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Imported item 10
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Imported item 12
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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.

D

Imported item 21

Define-XML is a CDISC standard for describing the metadata accompanying tabular study datasets. It explains what datasets and variables mean, how values are represented and where information originates. In a regulatory package, it helps reviewers interpret SDTM or ADaM data without relying on variable names alone.

Key Takeaways

  • Define-XML describes datasets; it is not a replacement for the observations contained in them.
  • Metadata should be consistent with the submitted datasets, controlled terminology and supporting documentation.
  • Check the receiving authority’s standards catalogue rather than assuming every version is accepted for every submission.

What Does Define-XML Describe?

ElementMeaning
StructureDatasets, variables and data types
MeaningLabels and controlled value lists
ProvenanceOrigins and relevant derivation information

CDISC describes Define-XML as a metadata exchange standard supporting study data review, including SDTM, SEND and ADaM. Version 2.1 adds explicit identification of referenced standards and controlled terminology versions, among other changes. A hypothetical analysis dataset might contain a treatment-group variable whose values are coded. The accompanying metadata would explain the codes, label the variable and describe its origin. This makes interpretation more reproducible, but does not prove that the dataset is accurate or that its disclosure is appropriate.

Primary guidance: CDISC Define-XML; CDISC Define-XML version 2.1.

Why It Matters for Data Protection

We recommend reviewing metadata alongside data when preparing a regulatory transfer. Variable labels, descriptions and derivation text should not accidentally expose personal information or point recipients towards an unrestricted identifying file. Check that origins and coding descriptions remain useful after any authorised transformation of the data. Assign a named owner to reconcile the final metadata with the final dataset, rather than generating the file once and assuming it remains current after programming changes. Keep validation results and resolved discrepancies with the submission records.

Frequently Asked Questions

Is Define-XML an analysis dataset?

No. It describes the datasets and their variables.

Does it cover both SDTM and ADaM?

Yes. CDISC describes its use with both models.

Does valid XML guarantee a good submission?

No. Technical validity does not establish semantic consistency, data quality or disclosure suitability.

Related reading: study data tabulation model sdtm; analysis data model adam.

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Updated: 27 October 2026