Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models.

ABSTRACT: We present generalized additive latent and mixed models (GALAMMs) for analysis of clustered data with responses and latent variables depending smoothly on observed variables. A scalable maximum likelihood estimation algorithm is proposed, utilizing the Laplace approximation, sparse matrix computation, and automatic differentiation. Mixed response types, heteroscedasticity, and crossed random effects are naturally incorporated into the framework. The models developed were motivated by applications in cognitive neuroscience, and two case studies are presented. First, we show how GALAMMs can jointly model the complex lifespan trajectories of episodic memory, working memory, and speed/executive function, measured by the California Verbal Learning Test (CVLT), digit span tests, and Stroop tests

SUBMITTER: Sorensen O 

PROVIDER: S-EPMC10188428 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature