Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data. (28th September 2018)

• Record Type: Journal Article
• Authors: Barnett, Ian; Torous, John; Staples, Patrick; Keshavan, Matcheri; Onnela, Jukka-Pekka
• Is Part Of: Journal of the American Medical Informatics Association. Volume 25:Number 12(2018)
• Page Start: 1669
• Page End: 1674
• Publication Date: 2018-09-28
• Subjects: digital phenotyping -- mHealth -- longitudinal data; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocy121
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objectives: As smartphones and sensors become more prominently used in mobile health, the methods used to analyze the resulting data must also be carefully considered. The advantages of smartphone-based studies, including large quantities of temporally dense longitudinally captured data, must be matched with the appropriate statistical methods in order draw valid conclusions. In this paper, we review and provide recommendations in 3 critical domains of analysis for these types of temporally dense longitudinal data and highlight how misleading results can arise from improper use of these methods. Target Audience: Clinicians, biostatisticians, and data analysts who have digital phenotyping data or are interested in performing a digital phenotyping study or any other type of longitudinal study with frequent measurements taken over an extended period of time. Scope: We cover the following topics: 1) statistical models using longitudinal repeated measures, 2) multiple comparisons of correlated tests, and 3) dimension reduction for correlated behavioral covariates. While these 3 classes of methods are frequently used in digital phenotyping data analysis, we demonstrate via actual clinical studies data that they may sometimes not perform as expected when applied to novel digital data.