N of 1 trial explained

An N of 1 trial (N=1) is a multiple crossover clinical trial, conducted in a single patient.^[1] A trial in which random allocation is used to determine the order in which an experimental and a control intervention are given to a single patient is an N of 1 randomized controlled trial. Some N of 1 trials involve randomized assignment and blinding, but the order of experimental and control interventions can also be fixed by the researcher.^[2]

This type of study has enabled practitioners to achieve experimental progress without the work of designing a group comparison study. This design, especially if including blinding and wash-out periods, can be effective in confirming causality. N-of-1 trials, if used in clinical practice to inform therapeutic decisions concerned with the patient participating in the trial, can be a source of evidence about individual treatment responses, fulfilling the promise of personalized medicine.^{[3] [4]}

Design

The N of 1 trials can be designed in many ways. For example, Single-Patient Open Trials (SPOTs) are located somewhere in between the formal (explanatory) N of 1 trials and the trial and error approach used in clinical practice and are characterized by at least one crossover period with washout in between. One of the most common procedures is the ABA withdrawal experimental design, where the patient problem is measured before a treatment is introduced (baseline) and then measured again during the treatment and finally when the treatment has terminated. If the problem vanished during the treatment it can be established that the treatment was effective. But the N=1 study can also be executed in an AB quasi experimental way; such type-2 N of 1 studies can be effective for testing treatments for severe, rare diseases when the expected effect of the intervention exceeds the effect size of confounders.^[5] Another variation is non-concurrent experimental design where different points in time are compared with one another. The standard approach to therapy choice, the trial and error method, may also be included in the N of 1 design.^[6] This experimental design also has a problem with causality, whereby statistical significance under a frequentist paradigm may be un-interpretable but other methods, such as clinical significance^[7] or Bayesian methods should be considered.

Many consider this framework to be a proof of concept or hypothesis generating process to inform subsequent, larger clinical trials.

List of variation in N of 1 trial

Design	Causality	Use
A-B	Quasi experiment	Often the only possible method
A-A^1-A	Experiment	Placebo design where A is no drug and A^1 is a placebo
A-B-A	Experiment	Withdrawal design where effects of B phase can be established
A-B-A-B	Experiment	Withdrawal design where effects of B phase can be established
A-B-A-B-A-B	Experiment	Withdrawal design where effects of B phase can be established
A-B^1-B^2-B^3-B^n-A	Experiment	Establishing the effect of different versions of B phase

Quasi experiment means that causality cannot be definitively demonstrated. Experiment means that it can be demonstrated.

Examples

An N of 1 trial is usually used to assess individual responses to treatments targeting chronic diseases.^[8] This design can be successfully implemented to determine optimal treatments for patients with diseases as diverse as osteoarthritis, chronic neuropathic pain and attention deficit hyperactivity disorder.^[9]

N of 1 designs can also be observational and describe natural intra-individual changes in health-related behaviours or symptoms longitudinally. N of 1 observational designs require complex statistical analysis of N of 1 data however, a simple 10-step procedure is available. ^[10] There has also been work to adapt causal inference counterfactual methods for using N of 1 observational studies to design subsequent N of 1 trials.^[11]

While N of 1 trials are increasing, results of a recent systematic review found that statistical analyses in these studies would improve with more methodological and statistical rigor across all periods of the studies.^[12]

The Quantified Self

With the cultural phenomenon of the quantified self a proliferation of personal experiments akin to N=1 is occurring, along with some detailed reports about them. This trend has been sparked in part by the growing ease of collecting data and analysing it, and also motivated by the ability of individuals to report such data easily.^[13]

A famous proponent and active experimenter was Seth Roberts, who reported on his self-experimental findings on his blog, and later published The Shangri-La Diet based on his conclusions from these self-experiments.

Global networks

The International Collaborative Network for N-of-1 Trials and Single-Case Designs (ICN)^[14] is a global network for clinicians, researchers and consumers who have an interest in these methods. There are over 400 members of the ICN who are based in over 30 countries across the globe. The ICN was established in 2017 and is co-chaired by Jane Nikles and Suzanne McDonald.

See also

• Applied behavior analysis
• B. F. Skinner
• Single-subject design
• Crossover study

Further reading

• Guyatt GH, Keller JL, Jaeschke R, Rosenbloom D, Adachi JD, Newhouse MT . The n-of-1 randomized controlled trial: clinical usefulness. Our three-year experience . Annals of Internal Medicine . 112 . 4 . 293–9 . February 1990 . 2297206 . 10.7326/0003-4819-112-4-293 .
• Johnston BC, Mills E . n-of-1 randomized controlled trials: an opportunity for complementary and alternative medicine evaluation . Journal of Alternative and Complementary Medicine . 10 . 6 . 979–84 . December 2004 . 15673992 . 10.1089/acm.2004.10.979 .
• Avins AL, Bent S, Neuhaus JM . Use of an embedded N-of-1 trial to improve adherence and increase information from a clinical study . Contemporary Clinical Trials . 26 . 3 . 397–401 . June 2005 . 15911473 . 10.1016/j.cct.2005.02.004 .
• Nikles CJ, Mitchell GK, Del Mar CB, Clavarino A, McNairn N . An n-of-1 trial service in clinical practice: testing the effectiveness of stimulants for attention-deficit/hyperactivity disorder . Pediatrics . 117 . 6 . 2040–6 . June 2006 . 16740846 . 10.1542/peds.2005-1328 . 20325906 .

Notes and References

1. Web site: Introduction to N-of-1 Trials: Indications and Barriers (Chapter 1) Effective Health Care (EHC) Program . 2023-12-31 . effectivehealthcare.ahrq.gov.
2. Punja . Salima . Bukutu . Cecilia . Shamseer . Larissa . Sampson . Margaret . Hartling . Lisa . Urichuk . Liana . Vohra . Sunita . August 2016 . N-of-1 trials are a tapestry of heterogeneity . Journal of Clinical Epidemiology . 76 . 47–56 . 10.1016/j.jclinepi.2016.03.023 . 1878-5921 . 27079847.
3. Serpico . Davide . Maziarz . Mariusz . 2023-12-14 . Averaged versus individualized: pragmatic N-of-1 design as a method to investigate individual treatment response . European Journal for Philosophy of Science . en . 13 . 4 . 59 . 10.1007/s13194-023-00559-0 . 1879-4920. free .
4. Nikles, J., & Mitchell, G. . Jane . Geoffrey . Nikles . Mitchell . 2015 . The Essential Guide to N-of-1 Trials in Health . SpringerLink . en . 10.1007/978-94-017-7200-6. 978-94-017-7199-3 . 33597874 .
5. Selker . Harry P. . Cohen . Theodora . D’Agostino . Ralph B. . Dere . Willard H. . Ghaemi . S. Nassir . Honig . Peter K. . Kaitin . Kenneth I. . Kaplan . Heather C. . Kravitz . Richard L. . Larholt . Kay . McElwee . Newell E. . Oye . Kenneth A. . Palm . Marisha E. . Perfetto . Eleanor . Ramanathan . Chandra . August 2022 . A Useful and Sustainable Role for N-of-1 Trials in the Healthcare Ecosystem . Clinical Pharmacology & Therapeutics . en . 112 . 2 . 224–232 . 10.1002/cpt.2425 . 0009-9236 . 9022728 . 34551122.
6. Book: Kravitz, R. L., Duan, N., Vohra, S., Li, J. . Introduction to N-of-1 trials: indications and barriers. Design and implementation of N-of-1 trials: A user's guide . AHRQ Publication No. 13(14)-EHC122-EF . 2014.
7. Chapple AG, Blackston JW . Finding Benefit in n-of-1 Trials . JAMA Internal Medicine . 179 . 3 . 453–454 . March 2019 . 30830189 . 10.1001/jamainternmed.2018.8379 . 73463184 .
8. Duan . Naihua . Kravitz . Richard L. . Schmid . Christopher H. . 2013-08-01 . Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research . Journal of Clinical Epidemiology . Methods for Comparative Effectiveness Research/Patient-Centered Outcomes Research: From Efficacy to Effectiveness . 66 . 8, Supplement . S21–S28 . 10.1016/j.jclinepi.2013.04.006 . 23849149 . 3972259 . 0895-4356.
9. Scuffham PA, Nikles J, Mitchell GK, Yelland MJ, Vine N, Poulos CJ, Pillans PI, Bashford G, del Mar C, Schluter PJ, Glasziou P . 6 . Using N-of-1 trials to improve patient management and save costs . Journal of General Internal Medicine . 25 . 9 . 906–13 . September 2010 . 20386995 . 2917656 . 10.1007/s11606-010-1352-7 . dead . https://archive.today/20130923221601/http://iospress.metapress.com/content/t51wg3207328hv38/?genre=article&issn=1387-2877&volume=21&issue=3&spage=967 . 2013-09-23 .
10. McDonald . S . Vieira . R . Johnston . D W. . Analysing N-of-1 observational data in health psychology and behavioural medicine: a 10-step SPSS tutorial for beginners . Health Psychology and Behavioral Medicine . 1 January 2020 . 8 . 1 . 32–54 . 10.1080/21642850.2019.1711096. 34040861 . 8114402 . free .
11. Daza EJ . Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials . Methods of Information in Medicine . 57 . 1 . e10–e21 . February 2018 . 29621835 . 6087468 . 10.3414/ME16-02-0044 . free .
12. Shaffer JA, Kronish IM, Falzon L, Cheung YK, Davidson KW . N-of-1 Randomized Intervention Trials in Health Psychology: A Systematic Review and Methodology Critique . Annals of Behavioral Medicine . 52 . 9 . 731–742 . August 2018 . 30124759 . 6128372 . 10.1093/abm/kax026 .
13. Swan M . The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery . Big Data . 1 . 2 . 85–99 . June 2013 . 27442063 . 10.1089/big.2012.0002 . free .
14. Web site: International Collaborative Network for N-of-1 Trials and Single-Case Designs . 2024-07-09 . N-of-1 and SCED . en.