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Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The objective of this work was to adapt and evaluate the performance of a Bayesian hybrid model to characterize objective temporal medication ingestion parameters from two clinical studies in patients with serious mental illness (SMI) receiving treatment with a digital medicine system. This system provides a signal from an ingested sensor contained in the dosage form to a patient-worn patch and transmits this signal via the patient\u2019s mobile device. A previously developed hybrid Markov-von Mises model was used to obtain maximum-likelihood estimates for medication ingestion behavior parameters for individual patients. The individual parameter estimates were modeled to obtain distribution parameters of priors implemented in a Markov chain-Monte Carlo framework. Clinical and demographic covariates associated with model ingestion parameters were also assessed. We obtained individual estimates of overall observed ingestion percent (median:75.9%, range:18.2\u201398.3%, IQR:32.9%), rate of excess dosing events (median:0%, range:0\u201314.3%, IQR:3.0%) and observed ingestion duration. The modeling also provided estimates of the Markov-dependence probabilities of dosing success following a dosing success or failure. The ingestion-timing deviations were modeled with the von Mises distribution. A subset of 17 patients (22.1%) were identified as prompt correctors based on Markov-dependence probability of a dosing failure followed by a dosing success of unity. The prompt corrector sub-group had a better overall digital medicine ingestion parameter profile compared to those who were not prompt correctors. Our results demonstrate the potential utility of a Bayesian Hybrid Markov-von Mises model for characterizing digital medicine ingestion patterns in patients with SMI.<\/jats:p>","DOI":"10.1038\/s41746-019-0095-z","type":"journal-article","created":{"date-parts":[[2019,3,22]],"date-time":"2019-03-22T11:02:47Z","timestamp":1553252567000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Evaluating digital medicine ingestion data from seriously mentally ill patients with a Bayesian Hybrid Model"],"prefix":"10.1038","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7032-4954","authenticated-orcid":false,"given":"Jonathan","family":"Knights","sequence":"first","affiliation":[]},{"given":"Zahra","family":"Heidary","sequence":"additional","affiliation":[]},{"given":"Timothy","family":"Peters-Strickland","sequence":"additional","affiliation":[]},{"given":"Murali","family":"Ramanathan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,3,22]]},"reference":[{"key":"95_CR1","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1002\/wps.20060","volume":"12","author":"JM Kane","year":"2013","unstructured":"Kane, J. 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