A Latent Factor Model for Spatial Data with Informative Missingness.

Factor analytic models of clustered multivariate data with informative censoring.

This article describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censoring process, and we account for dependency between these latent variables through a hierarchical model. A linear model...

LATENT VARIABLE MODELS FOR LONGITUDINAL STUDY WITH INFORMATIVE MISSINGNESS by

UvA - DARE ( Digital Academic Repository ) Planned missing designs to optimize the efficiency of latent growth parameter estimates

We examine the performance of planned missing (PM) designs for correlated latent growth curve models. Using simulated data from a model where latent growth curves are fitted to two constructs over five time points, we apply three kinds of planned missingness. The first is item-level planned missingness using a 3-form design at each wave such that 25% of data are missing. The second is wave-miss...

Growth modeling with nonignorable dropout: alternative analyses of the STAR*D antidepressant trial.

This article uses a general latent variable framework to study a series of models for nonignorable missingness due to dropout. Nonignorable missing data modeling acknowledges that missingness may depend not only on covariates and observed outcomes at previous time points as with the standard missing at random assumption, but also on latent variables such as values that would have been observed ...

Estimation of Random-Effects Model for Longitudinal Data with Nonignorable Missingness using Gibbs Sampling

The missing data problem is common in longitudinal or repeated measurements data. When the missingness mechanism is nonignorable, the distribution of the observed response and indicators of missingness should be modelled jointly using either ‘shared random-effects model’ or ‘correlated random-effects model’. However, computational challenges arise in the model fitting due to intractable numeric...