Epidemiology: National Comorbidity Survey Replication

This project proposes to study patterns of co-morbidity with the Grade of Membership (GoM) model, a statistical model for discrete data analysis. The data set contains dichotomous responses which provide presence or absence of 16 mental disorders from about 10,000 individuals in the U.S., and 250,000 individuals around the world. Assuming existence of extreme (basis) categories in the data, the GoM model postulates that individuals can have mixed membership in the extreme categories. The GoM model was developed in the 1970s and has been applied to a wide spectrum of health-related studies. This project proposes to use recently developed Bayesian estimation methods for analysis of co-morbidity patterns via the GoM model. Specifically, the goals of the proposal include: (1) to explore whether there is evidence of extreme categories in the data and whether patterns of co-morbidity are likely to exhibit mixed membership structure; (2) to estimate the GoM model parameters using Bayesian framework; and (3) to determine whether the GoM model provides a reasonable fit for the co-morbidity data.