The R package lcmm provides a series of functions to estimate statistical models based on
linear mixed model theory. It includes the estimation of mixed models and latent class mixed …

Two main classes of methodology have been developed for addressing the analytical
intractability of generalized linear mixed models: likelihood‐based methods and Bayesian …

Likelihood-based inference for longitudinal binary data can be obtained using a generalized
linear mixed model (Breslow, N. and Clayton, DG, 1993, Journal of the American Statistical …

The aim of this paper is to propose an algorithm to estimate linear mixed model when
random effect distribution is a mixture of Gaussians. This heterogeneous linear mixed model …

Joint models for a time-to-event (eg, survival) and a longitudinal response have generated
considerable recent interest. The longitudinal data are assumed to follow a mixed effects …

Mixed effects models have become very popular, especially for the analysis of longitudinal
data. One challenge is how to build a good enough mixed effects model. In this paper, we …

Abstract Breslow and Clayton (J Am Stat Assoc 88: 9–25, 1993) was, and still is, a highly
influential paper mobilizing the use of generalized linear mixed models in epidemiology and …

Maximum likelihood estimators for fixed effects and variance components in linear mixed
models, obtained under the assumption of normally distributed random effects, are shown to …

Joint models for longitudinal and survival data have gained a lot of attention in recent years,
with the development of myriad extensions to the basic model, including those which allow …

It is of great practical interest to simultaneously identify the important predictors that
correspond to both the fixed and random effects components in a linear mixed-effects (LME) …