Multivariate hidden semi-Markov models for longitudinal data: a dynamic regression modeling
Saiedeh Haji‐Maghsoudi, Majid Sadeghifar, Ghodratollah Roshanaei, Hossein Mahjub · Communications in Statistics - Simulation and Computation · 2022
The collection of multiple responses is common in longitudinal studies. It is of interest to assess the effects of covariates on multiple responses simultaneously using regression models. In some longitudinal studies, response variables are related to hidden states during the time-course study. The trend of responses for each subject can be segmented by hidden states. In the present study, we proposed a multivariate hidden semi-Markov regression model for longitudinal data having a multivariate normal distribution for responses in each hidden state. Based on the model, the effects of covariates on multiple responses have been calculated in the presence of hidden states. Simulation studies were conducted to evaluate the properties of the suggested model under different scenarios. The application of the suggested method was shown in hemodialysis patients’ data.