Mixed-Effects State-Space Models for Analysis of Longitudinal Dynamic Systems

State Space Representation of an Autoregressive Linear Mixed Effects Model for the Analysis of Longitudinal Data

Recently, we proposed an autoregressive linear mixed effects model for the analysis of longitudinal data in which the current response is regressed on the previous response, fixed effects, and random effects (Funatogawa et al., Statist. Med. 2007; 26:2113-2130). The model represents profiles approaching random equilibriums. Because intermittent missing is an inherent problem of the autoregressi...

Nonlinear mixed effects models for longitudinal data

State Space Mixed Models for Longitudinal Observations with Binary and Binomial Responses

State Space Mixed Models for Longitudinal Observations with Binary and Binomial Responses

We propose a new class of state space models for longitudinal discrete response data where the observation equation is specified in an additive form involving both deterministic and random linear predictors. These models allow us to explicitly address the effects of trend, seasonal or other time-varying covariates while preserving the power of state space models in modeling serial dependence in...

Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random e...