Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials.

ABSTRACT: Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed.Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personal

SUBMITTER: Daza EJ 

PROVIDER: S-EPMC6087468 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature