Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. ![]() 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. 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.
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