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Activity Number: 378 - LiDS Student Paper Award Winners: Topic-Contributed Papers
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Lifetime Data Science Section
Abstract #322322
Title: Combining Mixed Effects Hidden Markov Models with Latent Alternating Recurrent Event Processes to Model Diurnal Active-Rest Cycles
Author(s): Benny Ren* and Ian Barnett
Companies: University of Pennsylvania and University of Pennsylvania
Keywords: Alternating Recurrent Event Processes; Maximization Algorithm; Hidden Markov Models; Latent Variable Modeling; Longitudinal data; mHealth
Abstract:

Data collected from wearable devices and smartphones can shed light on an individual's patterns of behavior and circadian routine. Phone use can be modeled as alternating between the state of active use and the state of being idle. Markov chains and alternating recurrent event models are commonly used to model state transitions in cases such as these, and the incorporation of random effects can be used to introduce time-of-day effects. While state labels can be derived prior to modeling dynamics, this approach omits informative regression covariates that can influence state memberships. We instead propose a recurrent event proportional hazards (PH) regression to model the transitions between latent states. We propose an Expectation-Maximization (EM) algorithm for imputing latent state labels and estimating regression parameters. We show that our E-step simplifies to the hidden Markov model (HMM) forward-backward algorithm, allowing us to recover a HMM in addition to PH models. We derive asymptotic distributions for our model parameter estimates and compare our approach against competing methods through simulation as well as in a digital phenotyping study.


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

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