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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.

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Analysis Data Model (ADaM)

The Analysis Data Model (ADaM) is a CDISC standard defining how clinical trial data should be structured to support statistical analysis and regulatory review. ADaM datasets are derived from SDTM datasets and are "analysis-ready": each dataset is built so that a statistical result in the clinical study report can be reproduced with minimal programming, with full traceability back to the collected data.

Core ADaM structures include the Subject-Level Analysis Dataset (ADSL), Basic Data Structure (BDS) datasets for endpoints and laboratory values, and Occurrence Data Structure (OCCDS) datasets for adverse events and concomitant medications. Submission of ADaM datasets is required by the FDA and Japan's PMDA, and accepted by the EMA.

ADaM datasets contain subject-level key-coded health data. They are pseudonymised, not anonymous: the sponsor retains the ability to re-identify through the investigator site. Sharing ADaM datasets with regulators in the US or with data-sharing platforms therefore constitutes an international data transfer of personal data and must be covered in the trial's DPIA and consent documentation.