Marginalized models are likelihood-based marginal models for the analysis of longitudinal categorical data that were originally developed for longitudinal ...

Marginalized models (Heagerty, 1999, 2002) are often used for short longitudinal series when population averaged effects are of interest.

Marginalized models (Heagerty, 1999, 2002) are often used for short longitudinal series when population averaged effects are of interest.

Marginalized models for longitudinal count data · Bayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit Models · Bayesian ...

Abstract: Marginalized models (Heagerty, 1999, 2002) are often used for short longitudinal series when population averaged effects are of interest. Lee and ...

Analysis of long series of longitudinal ordinal data using marginalized models. Keunbaik Lee, Insuk Sohn, Donguk Kim. PII: S0167-9473(15)00167-X. DOI: http ...

In this paper, we introduce an extension of the MREM to accommodate longitudinal ordinal data. Maximum marginal likelihood estimation is implemented utilizing ...

In this section, we review Heagerty's marginalized model for the analysis of longitudinal binary data (Heagerty, 2002). Let Yi = (Yi1,...,Yini ) be binary data ...

New methods are proposed for modeling longitudinal categorical data with nonignorable dropout using marginalized transition models and shared random effects ...

These models permit direct interpretation of marginal mean parameters and characterize the serial dependence of longitudinal outcomes by using one of the ...