Estimation of extended mixed models using latent classes and latent processes: the R package lcmm

TL;DR

The paper introduces the R package lcmm, which implements advanced mixed models including latent class and joint models for longitudinal and survival data, with comprehensive estimation and post-fit analysis tools.

Contribution

It provides a unified software framework for estimating a wide range of complex mixed models using maximum likelihood with detailed implementation and example applications.

Findings

Successful estimation of various mixed models on cognitive aging data

Implementation of a modified Marquardt algorithm for stable convergence

Availability of extensive post-fit analysis functions

Abstract

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 models for Gaussian longitudinal outcomes (hlme), curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear multivariate outcomes (multlcmm), as well as joint latent class mixed models (Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a time-to-event that can be possibly left-truncated right-censored and defined in a competing setting. Maximum likelihood esimators are obtained using a modified Marquardt algorithm with strict convergence criteria based on the parameters and likelihood stability, and on the negativity of the second derivatives. The package also provides various post-fit functions including goodness-of-fit analyses, classification, plots, predicted trajectories, individual dynamic prediction of the event and predictive accuracy assessment. This paper constitutes a companion paper to the package by introducing each family of models, the estimation technique, some implementation details and giving examples through a dataset on cognitive aging.