Paper Info
Title
Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data.
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.
Year
DOI
Venue
2018
10.1093/jamia/ocy121
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Keywords
Field
DocType
digital phenotyping,mHealth,longitudinal data
Data science,Data mining,Medicine
Journal
Volume
Issue
ISSN
25
12
1067-5027
Citations 
PageRank 
References 
0
0.34
1
Authors
5
Name
Order
Citations
PageRank
Ian Barnett100.68
John Torous212.71
Patrick Staples300.34
Matcheri S Keshavan421.74
Jukka-pekka Onnela547536.55