2017/06/29 by Elias Chaibub Neto, Neto, Elias Chaibub, Thanneer M Perumal +9
Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #stat.AP
paper · pdf · doi:10.48550/arxiv.1706.09574
27 pages, 12 figures
arxiv created 2017/06/29 · arxiv updated 2017/06/30
In this work we provide a couple of contributions to the analysis of longitudinal data collected by smartphones in mobile health applications. First, we propose a novel statistical approach to disentangle personalized treatment and "time-of-the-day" effects in observational studies. Under the assumption of no unmeasured confounders, we show how to use conditional independence relations in the data in order to determine if a difference in performance between activity tasks performed before and after the participant has taken medication, are potentially due to an effect of the medication or to a "time-of-the-day" effect (or still to both). Second, we show that smartphone data collected from a given study participant can represent a "digital fingerprint" of the participant, and that classifiers of case/control labels, constructed using longitudinal data, can show artificially improved performance when data from each participant is included in both training and test sets. We illustrate our contributions using data collected during the first 6 months of the mPower study.