UI Cancer Center Biostatistics and Bioinformatics Monthly Seminar

“Latent Class Survival Model for Subgroup Identification and Risk Prediction Using High-Dimensional Genomic Features”

Jiehuan Sun
Assistant Professor of Biostatistics, Division of Epidemiology and Biostatistics, School of Public Health, UIC
Biostatistics Shared Resource Core, University of Illinois Cancer Center

Abstract:
Subgroup identification and risk prediction are two important tasks in disease studies, especially in complex diseases such as cancers, where patients are highly heterogeneous. Accurate identification of subgroups could be beneficial in disease diagnosis, treatment, as well as prognosis (i.e. risk prediction). Many statistical methods for subgroup identification and risk prediction using genomic features have been developed. However, most existing methods treat these two tasks independently. Some methods first classify patients into subgroups based on the genomic features and then study the survival outcomes of different subgroups using standard analysis. Other methods only focus on developing survival models using genomic features for risk prediction without considering patient heterogeneities. These methods might not be optimal, because survival outcome and subgroup structure are related to each other while they only consider one part of the information in their models. In this talk, I will discuss a novel latent class survival model, where subgroup structure and survival outcome are modelled simultaneously. Specifically, the subgroup structure in our model depends on both genomic features and survival outcome. Since the survival outcome is one part of our model, risk prediction can be easily done. Furthermore, regularization is incorporated in our model to accommodate the high-dimensional nature of genomic features.

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