A joint marginalized multilevel model for longitudinal outcomes

MULTILEVEL JOINT ANALYSIS OF LONGITUDINAL AND BINARY OUTCOMES by Seo

A Semiparametric Marginalized Model for Longitudinal Data with Informative Dropout.

We propose a marginalized joint-modeling approach for marginal inference on the association between longitudinal responses and covariates when longitudinal measurements are subject to informative dropouts. The proposed model is motivated by the idea of linking longitudinal responses and dropout times by latent variables while focusing on marginal inferences. We develop a simple inference proced...

Beta - Binomial and Ordinal Joint Model with Random Effects for Analyzing Mixed Longitudinal Responses

The analysis of discrete mixed responses is an important statistical issue in various sciences. Ordinal and overdispersed binomial variables are discrete. Overdispersed binomial data are a sum of correlated Bernoulli experiments with equal success probabilities. In this paper, a joint model with random effects is proposed for analyzing mixed overdispersed binomial and ordinal longitudinal respo...

A Joint Model with Marginal Interpretation for Longitudinal Continuous and Time-to-event Outcomes

This paper proposes a marginalized joint model for longitudinal continuous and repeated time-to-event outcomes, extending work of Njagi et al. (2012), as well as a marginalized joint model for bivariate repeated time-to-event outcomes.

A marginalized conditional linear model for longitudinal binary data when informative dropout occurs in continuous time

Within the pattern-mixture modeling framework for informative dropout, conditional linear models (CLMs) are a useful approach to deal with dropout that can occur at any point in continuous time (not just at observation times). However, in contrast with selection models, inferences about marginal covariate effects in CLMs are not readily available if nonidentity links are used in the mean struct...