CONSORT statement of N-of-1 trials (extension of the CONSORT statement)

The CONSORT Extension for Reporting N-of-1 Trials (CENT) provides a structured framework for reporting N-of-1 trials, extending established CONSORT reporting principles to this specialised trial design. It aims to improve the transparency, completeness and reproducibility of published N-of-1 trial reports by ensuring that key methodological, clinical and statistical information is clearly described.

N-of-1 trials can be particularly relevant in clinical situations where patient responses to treatments vary substantially or where recruitment of sufficiently large study populations is difficult. The CENT recommendations support researchers in reporting individual participant interventions, outcome measurements, analysis methods and interpretation of results in a consistent manner, allowing findings to be appropriately evaluated and potentially combined across studies.

This resource is particularly relevant for rare disease clinical research because many rare diseases are characterised by very small patient populations, clinical heterogeneity and limited opportunities for conventional randomised controlled trials. In these settings, N-of-1 trials and other personalised trial approaches may provide important evidence about treatment effects at the individual patient level. High-quality reporting is essential to ensure that these valuable observations contribute to the wider evidence base and inform future research.

For rare disease investigators, the CENT Statement can support responsible dissemination by improving the quality of scientific publications and facilitating knowledge sharing between researchers, clinicians, patients and other stakeholders. It complements broader reporting standards such as CONSORT and SPIRIT by addressing the specific needs of innovative and small population trial designs.

Although developed for N-of-1 trials rather than rare diseases specifically, the principles are broadly applicable to rare disease studies involving personalised approaches, adaptive designs or evidence generation in very small populations.