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[1] 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-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.

Plot of a possible dataset from an A-A¹-A N-of-1 trial: Imagine that during day 1-30, day 61-90, and day 121-150, the participant is taking a drug developed to treat high blood pressure. They are taking a placebo in the remaining time. Normal systolic pressure is slightly below 120 (in mmHg).

Examples

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.[2]

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. [3] 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.[4]

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.[5]

The Quantified Self

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.[6]

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) 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 A/Prof. Jane Nikles and Dr Suzanne McDonald.

References

Chapple AG, Blackston JW (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, et al. (September 2010). "Using N-of-1 trials to improve patient management and save costs". Journal of General Internal Medicine. 25 (9): 906–13. 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. PMC 8114402.
Daza EJ (February 2018). "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. doi:10.3414/ME16-02-0044. PMC 6087468. PMID 29621835.
Shaffer JA, Kronish IM, Falzon L, Cheung YK, Davidson KW (August 2018). "N-of-1 Randomized Intervention Trials in Health Psychology: A Systematic Review and Methodology Critique". Annals of Behavioral Medicine. 52 (9): 731–742. doi:10.1093/abm/kax026. PMC 6128372. PMID 30124759.
Swan M (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.