Dynamic modelling of n-of-1 data: powerful and flexible data analytics applied to individualised studies

Rute Vieira, Suzanne McDonald, Vera Araújo-Soares, Falko F Sniehotta, Robin Henderson

Research output: Contribution to journalArticle

22 Citations (Scopus)
5 Downloads (Pure)

Abstract

N-of-1 studies are based on repeated observations within an individual or unit over time and are acknowledged as an important research method for generating scientific evidence about the health or behaviour of an individual. Statistical analyses of n-of-1 data require accurate modelling of the outcome while accounting for its distribution, time-related trend and error structures (e.g., autocorrelation) as well as reporting readily usable contextualised effect sizes for decision-making. A number of statistical approaches have been documented but no consensus exists on which method is most appropriate for which type of n-of-1 design. We discuss the statistical considerations for analysing n-of-1 studies and briefly review some currently used methodologies. We describe dynamic regression modelling as a flexible and powerful approach, adaptable to different types of outcomes and capable of dealing with the different challenges inherent to n-of-1 statistical modelling. Dynamic modelling borrows ideas from longitudinal and event history methodologies which explicitly incorporate the role of time and the influence of past on future. We also present an illustrative example of the use of dynamic regression on monitoring physical activity during the retirement transition. Dynamic modelling has the potential to expand researchers' access to robust and user-friendly statistical methods for individualised studies.

Original languageEnglish
Pages (from-to)222-234
Number of pages13
JournalHealth Psychology Review
Volume11
Issue number3
Early online date6 Jul 2017
DOIs
Publication statusPublished - Sep 2017

Keywords

  • Decision Making
  • Humans
  • Models, Statistical
  • Observational Studies as Topic
  • Research Design
  • Journal Article
  • Research Support, Non-U.S. Gov't

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