We are pleased to announce that Associate Professor Sijian Wang has obtained new R01 funding for his project entitled "Heterogeneous and Robust Survival Analysis in Genomic Studies". The four year project aims to develop, evaluate, and disseminate powerful and computationally-efficient statistical methods to model the heterogeneity in both patients and biomarkers in genomic studies, and to enhance the ability of biomarker detection and prediction power. It is funded by the National Human Genome Research Institute (NHGRI).
About the figure: Illustration of the performance of our proposed regularized mixture Cox regression on TCGA ovarian cancer dataset. The motivation of the regularized mixture Cox regression is to address the possible heterogeneity in data. Specifically, it automatically divide the whole population into several clusters. Simultaneously, it fits a (different) Cox model in each cluster respectively. As shown in the figure, our regularized mixture Cox regression divides patients into three clusters: High-Risk” cluster, “Medium-Risk” cluster and “Low-Risk” cluster. The left panel shows the estimated smoothed hazard function for each cluster. The middle panel shows the Kaplan-Meier curves of three clusters, which are well separated. The right panel shows the estimated regression coefficients of twelve patient-specific index scores (PSRPs) derived from gene expression data. Clearly, the estimated coefficients vary much among three clusters, indicating our hypothesized heterogeneity that different genes play roles in different clusters (patients).