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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

Data minimisation

Data minimisation is the principle in Art. 5(1)(c) GDPR that personal data must be adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed. It requires the controller to ask, for each data element, whether the purpose can be achieved without it, with less of it, or with a less identifying version of it.

The principle applies at every stage: design of a protocol and eCRF (collect only variables the statistical analysis plan needs), choice of identifiers (subject codes rather than initials and full dates of birth), configuration of apps and devices (no unnecessary permissions or background collection), and retention (delete or anonymise once the purpose is fulfilled). It is a core component of data protection by design and is expressly reinforced for high-risk AI systems by the data governance requirements of the EU AI Act.

Minimisation is regularly tested by regulators in clinical research: collecting full birth dates when age suffices, free-text fields that capture unnecessary detail, or copying entire medical records into a research database are frequent audit findings. It should be documented in the DPIA and revisited whenever a protocol amendment adds new data points.