Exploration of model misspecification in latent class methods for longitudinal data: Correlation structure matters.

ABSTRACT: Modeling longitudinal trajectories and identifying latent classes of trajectories is of great interest in biomedical research, and software to identify latent classes of such is readily available for latent class trajectory analysis (LCTA), growth mixture modeling (GMM) and covariance pattern mixture models (CPMM). In biomedical applications, the level of within-person correlation is often non-negligible, which can impact the model choice and interpretation. LCTA does not incorporate this correlation. GMM does so through random effects, while CPMM specifies a model for within-class marginal covariance matrix. Previous work has investigated the impact of constraining covariance structures, both within and across classes, in GMMs-an approach often used to solve convergence problems. Using si

SUBMITTER: Neely ML 

PROVIDER: S-EPMC10777323 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature