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.

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Data quality

Data quality, in the data protection sense, refers to the set of requirements in Art. 5(1) GDPR that govern the substance of personal data rather than the legitimacy of processing them: data must be accurate and kept up to date, adequate, relevant and limited to what is necessary (data minimisation), and kept no longer than necessary (storage limitation). The term originates in Directive 95/46/EC and the OECD Guidelines and is mirrored in Art. 4 of Regulation (EU) 2018/1725 for EU institutions.

In life sciences, data protection quality requirements coexist with the data integrity expectations of GCP and of the EMA and FDA, summarised by the ALCOA+ acronym: attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring and available. Both frameworks converge on audit trails, controlled corrections, validated systems and clear ownership of data.

Practical data quality controls in a trial include edit checks in the eCRF, source data verification, query management, reconciliation between safety and clinical databases, and database lock procedures. From the data subject's side, quality is enforced through the right of rectification, which in a research context is exercised through documented corrections rather than by overwriting original records.