To facilitate cross-disciplinary collaboration that will enhance idiographic data collection and analysis procedures across health disciplines. We call this statistical field esametry.
To advance our Mission, we created this blog to help esametrically inclined statistics professionals connect by posting about their work. To do this, we’ll discuss:
- Theory: Statistics and biostatistics, psychology/behavior change, psychometrics, data science, machine learning, bioinformatics, and bioengineering methodologists are encouraged to share their ideas for building or adapting techniques that meet the needs of digital health investigators (e.g., personalized medicine across medical disciplines, health behavior psychologists and clinicians, chronic disease researchers, n-of-1 trialists, self-trackers, self-researchers).
- Applications: Health investigators are encouraged to share their experiences in designing studies and analyzing data. This will help us methodologists hone in on the techniques needed in applications of greatest interest.
Quantitative health philosophers, ethicists, anthropologists, and others so inclined are also most welcome to contribute! Over time, we hope these conversations will help define the field of esametry, and thereby foster sustained development of this field of digital WISDOM.
Digital WISDOM: Esametry
Esametry is the application of statistics to a single person, individual, or unit. Think of how econometrics, psychometrics, and biostatistics are the respective applications of statistics to economics, psychological measurement, and clinical trials and population health. Esametry is a quantitative idiographic (i.e., individualized/personalized) approach.
Background and Vision
An n-of-1, single-case, or single-subject 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.
The study can be observational or experimental. In the biomedical and clinical literature, a common example of the latter is an n-of-1 trial, also called an n-of-1 clinical trial or n-of-1 randomized controlled trial. In the health psychology, education, and behavior literature on idiographic approaches, a common example is a single-case or single-subject experimental design (SCED). At Stats-of-1, we refer to both of these experimental studies (as well as their observational counterparts) as types of within-individual statistical designs or methods, or WISDOM.
Our vision is to unite the various statistical methods focused on studying intra-individual cyclic patterns using digital health data under one statistical field of study. We call this field esametry (pronounced “ee-sa-met-ree”). This term is derived from isa (pronounced “ee-SA"), the Tagalog Filipino word for “one”. For more on how we derived this term, see this post.
Esametry sits at the center of digital WISDOM. It is:
- Grounded in biostatistics and causal inference for WISDOM, but spans many domains of statistics and quantitative psychology. Biostatistics domains include missing data, longitudinal analysis, functional data analysis, accelerometry, machine learning, series-of-n-of-1 analysis, and meta-analysis. Quantitative psychology domains include SCED effect sizes, multiple baseline designs, masked visual analysis, and randomization tests. 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.
- 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.
- Focused on human individuals for now. However, esametry may eventually be extended past this initial scope. Specifically, a more abstract “individual” might be expanded in the future to refer to an athlete, audience member, shopper, body part, animal, vehicle, sports team, organization, political party, institution, country, geographic region, financial instrument, recurrent group behavior, geologic phenomenon, etc.
- Short Post (iksi): at least every month
- Long Post (haba): occasional