Bayesian non-homogeneous hidden Markov model with variable selection for investigating drivers of seizure risk cycling

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dc.citation.firstpage333en_US
dc.citation.issueNumber1en_US
dc.citation.journalTitleThe Annals of Applied Statisticsen_US
dc.citation.lastpage356en_US
dc.citation.volumeNumber17en_US
dc.contributor.authorWang, Emily T.en_US
dc.contributor.authorChiang, Sharonen_US
dc.contributor.authorHaneef, Zulfien_US
dc.contributor.authorRao, Vikram R.en_US
dc.contributor.authorMoss, Roberten_US
dc.contributor.authorVannucci, Marinaen_US
dc.date.accessioned2023-03-27T20:16:35Zen_US
dc.date.available2023-03-27T20:16:35Zen_US
dc.date.issued2023en_US
dc.description.abstractA major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies which are a stochastic measurement of seizure risk. We consider a Bayesian nonhomogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation techniques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the Seizure TrackerTMTM system, a patient-reported electronic seizure diary. We report on the dynamics of seizure risk cycling, including validation of several known pharmacologic relationships. We also uncover novel findings characterizing the presence and volatility of risk states in Dravet syndrome which may directly inform counseling to reduce the unpredictability of seizures for patients with this devastating cause of epilepsy.en_US
dc.identifier.citationWang, Emily T., Chiang, Sharon, Haneef, Zulfi, et al.. "Bayesian non-homogeneous hidden Markov model with variable selection for investigating drivers of seizure risk cycling." <i>The Annals of Applied Statistics,</i> 17, no. 1 (2023) Project Euclid: 333-356. https://doi.org/10.1214/22-AOAS1630.en_US
dc.identifier.digitalAOAS1630_publisheden_US
dc.identifier.doihttps://doi.org/10.1214/22-AOAS1630en_US
dc.identifier.urihttps://hdl.handle.net/1911/114540en_US
dc.language.isoengen_US
dc.publisherProject Eucliden_US
dc.titleBayesian non-homogeneous hidden Markov model with variable selection for investigating drivers of seizure risk cyclingen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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