AR(1) latent class models for longitudinal count data.

ABSTRACT: In a variety of applications involving longitudinal or repeated-measurements data, it is desired to uncover natural groupings or clusters that exist among study subjects. Motivated by the need to recover clusters of longitudinal trajectories of conduct problems in the field of developmental psychopathology, we propose a method to address this goal when the response data in question are counts. We assume the subject-specific observations are generated from a first-order autoregressive process that is appropriate for count data. A key advantage of our approach is that the class-specific likelihood function arising from each subject's data can be expressed in closed form, circumventing common computational issues associated with random effects models. To further improve computational efficien

SUBMITTER: Henderson NC 

PROVIDER: S-EPMC6528786 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature