N of 1 trial

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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]

Contents

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. [5] 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. [6] 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. [7] 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 [8] 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

DesignCausalityUse
A-BQuasi experimentOften the only possible method
A-A1-AExperimentPlacebo design where A is no drug and A1 is a placebo
A-B-AExperimentWithdrawal design where effects of B phase can be established
A-B-A-BExperimentWithdrawal design where effects of B phase can be established
A-B-A-B-A-BExperimentWithdrawal design where effects of B phase can be established
A-B1-B2-B3-Bn-AExperimentEstablishing 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 synthetic dataset from an A-A -A N-of-1 trial: 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). Single subject blood pressure example.png
Plot of a synthetic dataset from an A-A -A N-of-1 trial: 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 is usually used to assess individual responses to treatments targeting chronic diseases. [9] 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. [10]

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. [11] 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. [12]

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. [13]

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. [14]

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) [15] 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

References

  1. ↑ "Introduction to N-of-1 Trials: Indications and Barriers (Chapter 1) | Effective Health Care (EHC) Program". effectivehealthcare.ahrq.gov. Retrieved 2023-12-31.
  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. doi:10.1016/j.jclinepi.2016.03.023. ISSN   1878-5921. PMID   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. 13 (4): 59. doi: 10.1007/s13194-023-00559-0 . ISSN   1879-4920.
  4. ↑ Nikles, J., & Mitchell, G. (2015). Nikles, Jane; Mitchell, Geoffrey (eds.). "The Essential Guide to N-of-1 Trials in Health" (PDF). SpringerLink. doi:10.1007/978-94-017-7200-6. ISBN   978-94-017-7199-3. S2CID   33597874.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  5. ↑ Smith, Jane; Yelland, Michael; Del Mar, Christopher (2015), Nikles, Jane; Mitchell, Geoffrey (eds.), "Single Patient Open Trials (SPOTs)" , The Essential Guide to N-of-1 Trials in Health, Dordrecht: Springer Netherlands, pp. 195–209, doi:10.1007/978-94-017-7200-6_15, ISBN   978-94-017-7200-6 , retrieved 2023-12-31
  6. ↑ 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. 112 (2): 224–232. doi:10.1002/cpt.2425. ISSN   0009-9236. PMC   9022728 . PMID   34551122.
  7. ↑ Kravitz, R. L., Duan, N., Vohra, S., Li, J. (2014). 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.{{cite book}}: CS1 maint: multiple names: authors list (link)
  8. ↑ 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. S2CID   73463184.
  9. ↑ 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. doi:10.1016/j.jclinepi.2013.04.006. ISSN   0895-4356. PMC   3972259 . PMID   23849149.
  10. ↑ 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.
  11. ↑ 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 . PMID   34040861.
  12. ↑ 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.
  13. ↑ 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.
  14. ↑ 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.
  15. ↑ "International Collaborative Network for N-of-1 Trials and Single-Case Designs". N-of-1 and SCED. Retrieved 2024-07-09.

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