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653 – Finite Mixture and Random Effects Models
A Finite Mixture Logistic-Gaussian Model for Zero-Inflated Clustered Binary Data
Sponsor: Biometrics Section
Keywords: Clustered binary data, Logistic-Gaussian model, random effect models, zero-inflated models, Gaussian quadratures
John Kwagyan
Howard University
Victor Apprey
Howard University
We establish a finite mixture model for clustered binary data in which all members of clusters in one latent class have a zero response with probability one; and clusters in the other latent class yield correlated outcomes. Response probabilities in terms of fixed effect and random effects models are formulated, and estimation procedures based on Gaussian Quadrature and a combined EM with Gaussian Quadrature are developed. Application to esophageal cancer data in Chinese families is presented.