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Activity Number: 281 - New Methods with Applications in Mental Health Statistics
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 10:30 AM to 12:20 PM
Sponsor: Mental Health Statistics Section
Abstract #322947
Title: Testing Unit Root Non-Stationarity in the Presence of Missing Data in Univariate Time Series of MHealth Studies
Author(s): Charlotte Fowler* and Xiaoxuan Cai and Justin Baker and Jukka-Pekka Onnela and Linda Valeri
Companies: Columbia University and Columbia University and Harvard Medical School and Harvard T.H. Chan School of Public Health and Columbia University
Keywords: Missing Data; Time Series Analysis; Stationarity; mHealth

The use of digital devices to collect data in mobile health (mHealth) studies introduces a novel application of time series methods, with the constraint of potential data missing at random (MAR) or missing not at random (MNAR). In time series analysis, testing for stationarity is an important preliminary step to inform appropriate later analyses. The augmented Dickey-Fuller (ADF) test was developed to test the null hypothesis of unit root non-stationarity, under no missing data. Existing methods for time series with missing data such as complete case analysis, last observation carry forward, multiple imputation with chained equations, and linear interpolation impose constraints on the autocorrelation structure, and thus impact unit root testing. We propose maximum likelihood estimation and multiple imputation using a state space model approaches to adapt the ADF test to a context with missing data. We further develop sensitivity analysis techniques to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across different missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients.

Authors who are presenting talks have a * after their name.

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