Finite Mixtures of Multivariate t t Linear Mixed‐Effects Models for Censored Longitudinal Data With Concomitant Covariates

ABSTRACT Clustering longitudinal biomarkers in clinical trials uncovers associations between clinical outcomes, disease progression, and treatment effects. Finite mixtures of multivariate linear mixed‐effects (FM‐MtLME) models have proven effective for modeling and clustering multiple longitudinal trajectories that exhibit grouped patterns with strong within‐group similarity. Motivated by an AIDS study with plasma viral loads measured under assay‐specific detection limits, this article extends the FM‐MtLME model to account for censored outcomes. The proposed model is called the FM‐MtLME with censoring (FM‐MtLMEC). To allow covariate‐dependent mixing proportions, we further extend it with a logistic link, resulting in the EFM‐MtLMEC model. Two efficient EM‐based algorithms are developed for parameter estimation of both FM‐MtLMEC and EFM‐MtLMEC models. The utility of our methods is demonstrated through comprehensive analyses of the AIDS data and simulation studies.