Last updated: Feb 24, 2020
Thanks for stopping by! This blog is all about statistical approaches to collecting and analyzing data from an n-of-1 study. See our Theme and Mission below, which are also featured on the About page.
Posting Schedule: As a start, you can expect short (#iksi) posts every Monday, and occasional long (#haba) posts. You’ll also get posts from other authors, and maybe a few sporadic posts.
An n-of-1 study is an idiographic, within-individual study of one person’s recurrent characteristics and patterns under various exposure or treatment conditions. These exposure periods make up repeating intervals that can be accurately described as a set of partitioned time series of variables (e.g., outcomes and predictors). The core idea is that there are stable periodic or cyclic patterns that are predictable.
We call the theory and application of statistics for studying intra-individual cyclic patterns esametry (pronounced “ee-sa-metry”). This term is derived from isa (pronounced “ee-SA”), the Filipino-Tagalog word for “one”. Esametry is:
Grounded in biostatistics and causal inference, but spans many domains of statistics. To date, these may (or actually do) include missing data, longitudinal analysis, functional data analysis, accelerometry, machine learning, series-of-n-of-1 analysis, and meta-analysis. However, the unifying strand that defines this field is its focus on intra-individual cyclic patterns.
Related to periodic pattern analysis or similarity search. However, these are engineering techniques aimed at assessing similarity between multiple time series or their partitions (i.e., they do not focus on statistical estimation and inference). This aspect of esametry is perhaps more closely aligned with the statistics field of functional data analysis.
Comprised of technical methods for data collection and analysis, but is not limited in application to just health. That is, an “individual” need not be a patient—or even a human being. Rather, an “individual” could refer to an athlete, audience member, shopper, body part, animal, vehicle, sports team, organization, institution, country, geographic region, financial instrument, recurrent group behavior, geologic phenomenon, etc.
Most useful when the patterns of association or causal mechanisms of interest are known or suspected to be highly specific to each individual. That is, esametric methods should be used when average associations or mechanisms across individuals are ill-defined or do not exist.
To improve statistical and causal inference for each individual.
To advance our Mission, we created this blog to help esametrically inclined statistics professionals connect. To do this, we’ll discuss:
- Theory: Statistics, data science, and machine learning methodologists are encouraged to share ideas for building or adapting techniques that meet the needs of n-of-1 practitioners.
- Applications: N-of-1 practitioners are encouraged to share experiences in designing studies and analyzing data. This will help us methodologists hone in on the techniques needed in applications of greatest interest.
Over time, we hope these conversations will help define the field of esametry, and thereby foster sustained development of this field of expanded n-of-1 studies.