04: Autoregressive Linear Mixed Effects Models and Dynamic Models for Longitudinal Data Analysis

Music City Center 

Longitudinal data and panel data are both obtained by repeatedly measuring a certain response variable over time for multiple subjects, and analytical methods for these data have been developed separately in several disciplines. In recent years, methodological integration is occurring. Here, we focus on dynamic models in which the previous response value, that is the lagged dependent variable, appears on the right-hand side of the equation. We compare our proposed autoregressive linear mixed effects models (Funatogawa et al., 2007; Funatogawa and Funatogawa, 2019) with similar dynamic models used in several fields. Our proposed model is an extension of the linear mixed effects model by combining autoregression with it, and has been developed with the main aim of expressing changes in responses over time. We have also provided the state-space representation and the relationships with nonlinear mixed effects models. On the other hand, in panel data analysis, which is used in observational studies, stable unobservable individual characteristics and the lagged dependent variable are often used for adjustments purposes. In this study, we focus on likelihood-based methods.

Autoregressive

Dynamic

Longitudinal

Panel Data 

Biometrics Section