Penalized longitudinal mixed models with latent group structure, with an application in neurodegenerative diseases
Hatami, F.; Perrakis, K.; Cooper-Knock, J.; Mukherjee, S.; Dondelinger, F.
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SO_SCPLOWUMMARYC_SCPLOWLarge-scale longitudinal data are often heterogeneous, spanning latent subgroups such as disease subtypes. In this paper, we present an approach called longitudinal joint cluster regression (LJCR) for penalized mixed modelling in the latent group setting. LJCR captures latent group structure via a mixture model that includes both the multivariate distribution of the covariates and a regression model for the response. The longitudinal dynamics of each individual are modeled using a random effect intercept and slope model. Inference is done via a profile likelihood approach that can handle high-dimensional covariates via ridge penalization. LJCR is motivated by questions in neurodegenerative disease research, where latent subgroups may reflect heterogeneity with respect to disease presentation, progression and diverse subject-specific factors. We study the performance of LJCR in the context of two longitudinal datasets: a simulation study and a study of amyotrophic lateral sclerosis (ALS). LJCR allows prediction of progression as well as identification of subgroups and subgroup-specific model parameters.
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