Common predictor effects for multivariate longitudinal data

A mixed-effects regression model for longitudinal multivariate ordinal data.

A mixed-effects item response theory model that allows for three-level multivariate ordinal outcomes and accommodates multiple random subject effects is proposed for analysis of multivariate ordinal outcomes in longitudinal studies. This model allows for the estimation of different item factor loadings (item discrimination parameters) for the multiple outcomes. The covariates in the model do no...

Marginalized transition random effects models for multivariate longitudinal binary data

Generalized linear models with random effects and/or serial dependence are commonly used to analyze longitudinal data. However, interpretation and computation of marginal covariate effects can be difficult. Heagerty has proposed marginally specified logistic-normal models (1999) and marginalized transition models (2002) for longitudinal binary and categorical data in which the marginal mean is ...

Logistic Regression for Multivariate Longitudinal Ordinal Data

Dynamical Correlation for Multivariate Longitudinal Data

Nonparametric methodology for longitudinal data analysis is becoming increasingly popular. The analysis of multivariate longitudinal data, where data on several time courses are recorded per subject, has received considerably less attention, in spite of its importance for practical data analysis. In particular, there is a need for measures and estimates to capture dependency between the compone...

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...