Development of a mixture model allowing for smoothing functions of longitudinal trajectories.

ABSTRACT: In the health and social sciences, two types of mixture models have been widely used by researchers to identify participants within a population with heterogeneous longitudinal trajectories: latent class growth analysis and the growth mixture model. Both methods parametrically model trajectories of individuals, and capture latent trajectory classes, using an expectation-maximization algorithm. However, parametric modeling of trajectories using polynomial functions or monotonic spline functions results in limited flexibility for modeling trajectories; as a result, group membership may not be classified accurately due to model misspecification. In this paper, we propose a smoothing mixture model allowing for smoothing functions of trajectories using a modified algorithm in the M step. Specif

SUBMITTER: Ding M 

PROVIDER: S-EPMC8009804 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature