N-of-1 trials in the medical literature: a systematic review

Med Care. 2011 Aug;49(8):761-8. doi: 10.1097/MLR.0b013e318215d90d.


Background: N-of-1 trials (multiple crossover studies conducted in single individuals) may be ideal for determining individual treatment effects and as a tool to estimate heterogeneity of treatment effects (HTE) in a population. However, comprehensive data on n-of-1 trial methodology and analysis is lacking. We performed this study to describe n-of-1 trial characteristics, examine treatment changes resulting from n-of-1 trial participation, and to determine if trial reporting is adequate for estimating HTE.

Methods: We undertook a systematic review of n-of-1 trials published between 1985 and December 2010. Included trials were those having individual treatment episodes as the unit of randomization and reporting individual-specific treatment effects. We abstracted trial characteristics, treatment change information, and analytic methods.

Results: We included 108 trials reporting on 2154 participants. Approximately half (49%) of the trials used a statistical cutoff to determine a superior treatment, whereas the remainder used a graphical comparison (25%) or a clinical significance cutoff (20%). Sixty-seven trials, reporting on 488 people, provided treatment change information: 54% of participants had subsequent treatment decisions consistent with the results of the trial, 8% had decisions inconsistent with trial results, and 38% had ambiguous results. Less than half of the trials (45%) reported adequate information to facilitate the calculation of HTE.

Conclusion: N-of-1 trials are a useful tool for enhancing therapeutic precision in a range of conditions and should be conducted more often. To facilitate future meta-analysis, and the estimation of HTE, researchers reporting n-of-1 trial results should clearly describe individual data.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review
  • Systematic Review

MeSH terms

  • Clinical Trials as Topic*
  • Cross-Over Studies*
  • Humans
  • Models, Statistical
  • Research Design*