Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM.

ABSTRACT: Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. The two hidden states included in the model were remission and exa

SUBMITTER: Brekkan A 

PROVIDER: S-EPMC6868114 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature