Assessing causal inference and disparity in the latent variable prediction framework

Chen-Pin Wang, Ph.D.
University of Texas-San Antonio

ABSTRACT:
Latent Growth Mixture Modeling (LGMM) is a useful statistical tool to characterize the heterogeneity of the longitudinal development of a prognostic variable using the so-called latent classes. Recently an advanced statistical learning methodology (Jo 2016) was developed to validate the scientific utility of the latent classes regarding the prediction of a target outcome of interest. This presentation focuses on deriving causal inference and health disparity in this prediction model framework. The proposed method involves LGMM analysis of the prognostic variable, validating the prediction of the latent classes for the distal (future) outcome of interest, and then incorporating the inverse propensity score weighting technique to deriving causal relationship between the prognostics classes with the distal outcome and the associated health disparity. I will demonstrate the proposed method using a longitudinal epidemiology study of patients with type 2 diabetes that aimed at assessing the prediction of glycemic control for cardiovascular diseases related hospitalization and the racial disparity in this relationship.