Properties of bootstrap tests for N-of-1 studies

Sharon X Lin, Leanne Morrison, Peter W F Smith, Charlie Hargood, Mark Weal, Lucy Yardley

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

N-of-1 study designs involve the collection and analysis of repeated measures data from an individual not using an intervention and using an intervention. This study explores the use of semi-parametric and parametric bootstrap tests in the analysis of N-of-1 studies under a single time series framework in the presence of autocorrelation. When the Type I error rates of bootstrap tests are compared to Wald tests, our results show that the bootstrap tests have more desirable properties. We compare the results for normally distributed errors with those for contaminated normally distributed errors and find that, except when there is relatively large autocorrelation, there is little difference between the power of the parametric and semi-parametric bootstrap tests. We also experiment with two intervention designs: ABAB and AB, and show the ABAB design has more power. The results provide guidelines for designing N-of-1 studies, in the sense of how many observations and how many intervention changes are needed to achieve a certain level of power and which test should be performed.

Original languageEnglish
Pages (from-to)276-290
Number of pages15
JournalBritish Journal of Mathematical and Statistical Psychology
Volume69
Issue number3
DOIs
Publication statusPublished - 6 Oct 2016

Keywords

  • Algorithms
  • Clinical Trials as Topic/methods
  • Computer Simulation
  • Data Interpretation, Statistical
  • Humans
  • Models, Statistical
  • Outcome Assessment (Health Care)/methods
  • Sample Size

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