N of 1 trial
An N of 1 trial is a clinical trial in which a single patient is the entire trial, a single case study. A trial in which random allocation can be used to determine the order in which an experimental and a control intervention are given to a patient is an N of 1 randomized controlled trial. The order of experimental and control interventions can also be fixed by the researcher.
This type of study has enabled practitioners to achieve experimental progress without the overwhelming work of designing a group comparison study. It can be very effective in confirming causality. This can be achieved in many ways. 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; this means that causality cannot be definitively demonstrated. Another variation is non-concurrent experimental design where different points in time are compared with one another. 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 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 trialEdit
|A-B||Quasi experiment||Often the only possible method|
|A-A1-A||Experiment||Placebo design where A is no drug and A1 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-B1-B2-B3-Bn-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.
An N of 1 trial can be successfully implemented to determine optimal treatments for patients with diseases as diverse as osteoarthritis, chronic neuropathic pain and attention deficit hyperactivity disorder.
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.  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. 
Recently, 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.
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.
- Chapple, Andrew Genius; Blackston, James Walker (1 March 2019). "Finding Benefit in n-of-1 Trials". JAMA Internal Medicine. 179 (3): 453–454. doi:10.1001/jamainternmed.2018.8379. PMID 30830189.
- Scuffham PA, Nikles J, Mitchell GK, Yelland MJ, Vine N, Poulos CJ, Pillans PI, Bashford G, del Mar C, Schluter PJ, Glasziou P (2010). "Using N-of-1 trials to improve patient management and save costs". Journal of General Internal Medicine. 25 (9): 906–913. doi:10.1007/s11606-010-1352-7. PMC 2917656. PMID 20386995. Archived from the original on 2013-09-23.
- McDonald, S; Vieira, R; Johnston, D W. (1 January 2020). "Analysing N-of-1 observational data in health psychology and behavioural medicine: a 10-step SPSS tutorial for beginners". Health Psychology and Behavioral Medicine. 8 (1): 32–54. doi:10.1080/21642850.2019.1711096.
- Daza, EJ (Feb 2018). "Causal analysis of self-tracked time series data using a counterfactual framework for N-of-1 trials". Methods of Information in Medicine. 57 (S 01): e10–e21. doi:10.3414/ME16-02-0044. PMC 6087468. PMID 29621835.
- Swan, Melanie (June 2013). "The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery". Big Data. 1 (2): 85–99. doi:10.1089/big.2012.0002. PMID 27442063.
- Guyatt GH, Keller JL, Jaeschke R, Rosenbloom D, Adachi JD, Newhouse MT (February 1990). "The n-of-1 randomized controlled trial: clinical usefulness. Our three-year experience". Annals of Internal Medicine. 112 (4): 293–9. doi:10.7326/0003-4819-112-4-293. PMID 2297206.
- Johnston BC, Mills E (December 2004). "N-Of-1 Randomized Controlled Trials: An Opportunity for Complementary and Alternative Medicine Evaluation". Journal of Alternative and Complementary Medicine. 10 (6): 979–84. doi:10.1089/acm.2004.10.979. PMID 15673992.
- Avins AL, Bent S, Neuhaus JM (June 2005). "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. doi:10.1016/j.cct.2005.02.004. PMID 15911473.
- Nikles CJ, Mitchell GK, Del Mar CB, Clavarino A, McNairn N (June 2006). "An n-of-1 trial service in clinical practice: testing the effectiveness of stimulants for attention-deficit/hyperactivity disorder". Pediatrics. 117 (6): 2040–6. doi:10.1542/peds.2005-1328. PMID 16740846. S2CID 20325906.