Evaluating digital medicine ingestion data from seriously mentally ill patients with a Bayesian Hybrid Model.

ABSTRACT: 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's 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 covari

SUBMITTER: Knights J 

PROVIDER: S-EPMC6550231 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature