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B1407
Title: Biomarker discovery in heterogeneous populations Authors:  Elizabeth Slate - Florida State University (United States) [presenting]
Junxian Geng - Florida State University (United States)
Abstract: Identification of valid, clinically relevant biomarkers for disease has potential to provide less invasive diagnostic tools, to enhance understanding of initiation and progression at the cellular level, and to guide development of new therapeutic agents. When the biomarkers are binary, logic regression provides a means to discover Boolean combinations of the markers strongly associated with outcome. The interpretability of these Boolean marker combinations and, potentially, additional interactions with environmental and behavioral characteristics, is appealing and can provide insight. However, complex diseases such as cancer that arise from multiple pathways and present at varying stages of development and progression can lead to hidden population heterogeneity in the biomarker-disease association. We describe an extension of logic regression for jointly modeling binary and continuous outcomes that uses a latent class structure to accommodate subpopulation heterogeneity. Estimation and inference are compared for two Bayesian semiparametric formulations using a variety of computational approaches.