The use of N-of-1 trials to generate real-world evidence for optimal treatment of individuals and populations

Abstract

Introduction: Ideally, real-world data (RWD) collected to generate real-world evidence (RWE) should lead to impact on the care and health of real-world patients. Deriving from care in which clinicians and patients try various treatments to inform therapeutic decisions, N-of-1 trials bring scientific methods to real-world practice. Methods: These single-patient crossover trials generate RWD and RWE by giving individual patients various treatments in a double-blinded way in sequential periods to determine the most effective treatment for a given patient. Results: This approach is most often used for patients with chronic, relatively stable conditions that provide the opportunity to make comparisons over multiple treatment periods, termed Type 1 N-of-1 trials. These are most helpful when there is heterogeneity of treatment effects among patients and no a priori best option. N-of-1 trials also can be done for patients with rare diseases, potentially testing only one treatment, to generate evidence for personalized treatment decisions, designated as Type 2 N-of-1 trials. With both types, in addition to informing individual’s treatments, when uniform protocols are used for multiple patients with the same condition, the data collected in the individual N-of-1 trials can be aggregated to provide RWD/RWE to inform more general use of the treatments. Thereby, N-of-1 trials can provide RWE for the care of individuals and for populations. Conclusions: To fulfill this potential, we believe N-of-1 trials should be built into our current healthcare ecosystem. To this end, we are building the needed infrastructure and engaging the stakeholders who should receive value from this approach.

Advancing regulatory science through real-world data and real-world evidence

“…Use of RWD to inform trial designs and generate RWE to evaluate safety and efficacy [1][2][3][4][5][6][7][8][9][10][11][12] Currently, most of the patient-derived RWD used in biomedical research resides in Electronic Health Records (EHRs). Even though their development was not intended for research purposes, the large volume of information, data richness, continuum of care data collection, and ability to be linked and integrated with additional data from medical encounters such as claims data, imaging, and laboratory information can provide a more comprehensive view of an individual and their health journey.…”

“…RWD leading to RWE has also been used as external control arms in interventional studies where randomization is impractical or unethical, and in different types of non-interventional studies. In this thematic issue, Selker et al describe how through a more controlled environment utilizing standardized study protocols, the information obtained from a single patient could also help elucidate wanted and unwanted treatment effects that could be used as RWE and inform clinical decision-making at the individual patient level or even more broadly, if data from other similar cases is aggregated [ 8 ]. A series of manuscripts in this thematic issue also describe and apply a causal roadmap that can serve as a guide for studies that generate RWE [ 1 , 2 , 11 ].…”