Bayesian Analysis of Multivariate Latent Curve Models With Nonlinear Longitudinal Latent Effects

Studying Multivariate Change Using Multilevel Models and Latent Curve Models.

In longitudinal research investigators often measure multiple variables at multiple points in time and are interested in investigating individual differences in patterns of change on those variables. In the vast majority of applications, researchers focus on studying change in one variable at a time. In this article we consider methods for studying relations1.1ips between patterns of change on ...

Latent Growth Curve Models

A Fully Bayesian Analysis of Multivariate Latent Class Models with an Application to Metric Conjoint Analysis

In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters deter...

Latent Curve Covariance Models for Longitudinal Data Marc

We develop a class of covariance models for longitudinal data which capture underlying stochastic variation in an interpretable manner via latent curves. The approach decomposes the variation into a small number of data-adaptive components, the latent curves, which w e call proto-splines. These components establish the shape of the stochastic variation and allow individual diierences to be expr...

Latent mixture models for multivariate and longitudinal outcomes.

Repeated measures and multivariate outcomes are an increasingly common feature of trials. Their joint analysis by means of random effects and latent variable models is appealing but patterns of heterogeneity in outcome profile may not conform to standard multivariate normal assumptions. In addition, there is much interest in both allowing for and identifying sub-groups of patients who vary in t...