What are Adaptive N of 1 Trials?

Clinical trials have long relied on standardised group-based research designs to test treatments, but these approaches do not capture individual patient responses to treatment. N of 1 trials are individualised experiments designed to evaluate and optimise treatment by systematically comparing different treatment options within the same patient. Now, with adaptive methods, N of 1 trials are becoming even more powerful. These designs allow real-time modifications based on accumulating data, which helps to refine treatment selection, reduce inefficiencies and improves clinical decision-making. 

The Rise of Adaptive Trials

Adaptive trials are rapidly gaining traction as a viable trial design in clinical research. According to a report by MTP Connect, the number of studies employing adaptive designs has more than doubled between 2020 and 2022 compared to the previous three years, reflecting increasing adoption. This momentum is being reinforced by strong government support. The 2023 Innovative Trials grant, funded through the Medical Research Future Fund (MRFF), allocated $23.7 million to promote the adoption of innovative designs. This backing signals a growing recognition of the value and credibility of adaptive methodologies. 

What Makes an N of 1 Trial Adaptive?

Adaptive N of 1 trials involve real-time modifications to the trial protocol based on interim data. While traditional N of 1 trials rely on fixed sequences, such as AB/BA crossover structures, adaptive trials offer greater flexibility by allowing changes to treatment allocation, sampling strategies, stopping rules and dose modifications as the trial progresses. This adaptability enables more efficient detection of treatment effects while preserving the trial’s focus on individualised care. 

Bayesian Adaptive Approaches

To support these dynamic designs, Bayesian statistical frameworks are particularly well-suited. Bayesian methods offer a structured way to incorporate prior knowledge and continuously update treatment effect estimates as new data becomes available. This is especially valuable in personalised medicine, where historical information or data  from similar patients can inform ongoing decision-making. For instance, Siththara Gedara et al. (2020) demonstrated how Bayesian adaptive methods estimate both population-level and individual treatment effects. By updating the probability distributions in response to patient outcomes, the approach refines treatment selection by in a data-driven,patient-centric manner. Another compelling example is the Bayesian-bandit model described by Shrestha and Jain (2021), which uses a dynamic reward system to allocate treatments. This model prioritises those showing greater effectiveness while still exploring alternatives, ultimately optimising clinical decisions in real time. 

Design Considerations

While adaptive N of 1 trials offer advantages, they also require careful planning to preserve scientific rigour. Adaptations should be pre-specified in the protocol to avoid reactive changes that introduce bias. Maintaining blinding and using appropriate randomisation techniques are essential to minimise bias during interim modifications. Statistical planning can be complex, especially around power and sample size, but Bayesian inference and simulation studies can help ensure robust results. Additionally, since adaptive designs are still relatively new in some settings, early engagement with regulatory bodies such as the Therapeutic Goods Administration, Food & Drug Administration, or European Medicines Agency, is recommended as relevant for sponsors of drugs or medical devices, to align with evolving guidelines and streamline trial approval processes.

Real-World Applications

The flexibility of adaptive N of 1 trials makes them especially valuable in contexts where treatment responses vary substantially across individuals. In the field of medical cannabis, for instance, patients with chronic pain or epilepsy often respond differently to cannabinoid-based therapies; adaptive N of 1 designs allow for optimisation of dosing based on real-time feedback on efficacy and tolerability. Similarly, digital health interventions, such as those using wearable technology, enable continuous patient monitoring, allowing dynamic adaptation of treatment in response to biometric signals. In complementary medicine, therapies like acupuncture or herbal supplements also benefit from adaptive frameworks that can accommodate patient-specific responses and preferences, enabling more precise evaluation of their effects. These adaptive approaches not only enhance individual care but also provide data that can inform broader clinical decision-making and policy. By tailoring interventions over time, they may improve adherence, reduce side effects, and support the delivery of more scalable, personalised care.

Future Scope

As computational tools and analytic methods continue to improve, adaptive N of 1 trials are poised to play a larger role in the future of personalised medicine. Machine learning and predictive analytics may further enhance their ability to refine treatments with precision, adapting in real time to individuals' needs. While challenges remain, particularly around the design complexity and regulatory acceptance, these trials offer a compelling model for delivering more targeted healthcare. By combining flexibility with methodological rigour, adaptive N of 1 trials exemplify the shift towards patient-centred research. As the field evolves, they are set to become a cornerstone of precision health. 

Ready to learn more?

While N of 1 trials offer significant benefits, they can present certain challenges that require careful attention. Designing and conducting an N of 1 trial demands detailed planning, particularly in managing the logistics of data collection and analysis. This process can be time-consuming, involving the development of clear protocols, managing patient adherence, and ensuring consistent outcome reporting. Analysing the data generated from N of 1 trials requires specialised statistical knowledge. Without experience, interpreting results accurately can be challenging. Having access to professionals skilled in N of 1 trials is essential to ensure successful outcomes.

At N-of-1 Hub, we offer expert guidance and training to help you navigate these complexities. We provide the expertise needed to design, conduct, and analyse N of 1 trials, whether you're a university researcher, health professional, or part of an organisation conducting clinical trials in pharmaceuticals, complementary medicines, medical devices, ASOs or medical cannabis. Our courses are also ideal for anyone interested in implementing these trial designs in clinical practice to deliver more personalised care and improve patient outcomes. 

Ready to get started? We can help you integrate N of 1 trials into your research or clinical practice, ensuring a more personalised approach to patient care. Click here to contact us.