Mixed-effects state-space models for analysis of longitudinal dynamic systems.

ABSTRACT: The rapid development of new biotechnologies allows us to deeply understand biomedical dynamic systems in more detail and at a cellular level. Many of the subject-specific biomedical systems can be described by a set of differential or difference equations that are similar to engineering dynamic systems. In this article, motivated by HIV dynamic studies, we propose a class of mixed-effects state-space models based on the longitudinal feature of dynamic systems. State-space models with mixed-effects components are very flexible in modeling the serial correlation of within-subject observations and between-subject variations. The Bayesian approach and the maximum likelihood method for standard mixed-effects models and state-space models are modified and investigated for estimating unknown par

SUBMITTER: Liu D 

PROVIDER: S-EPMC3507995 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature