American Statistical Association
Our work is motivated by analyzing TCGA ovarian cancer data with survival outcome. Our analysis has two aims. One is to identify important core pathways and important genes within the identified pathways related to ovarian cancer survival. The other is to build a predictive model for future patients' survival based on the identified genomic features. We propose two methods.
The first method is doubly regularized Cox regression (DrCox), which is based on the penalized partial likelihood estimation with a mixture of convex penalties. The convexity of objective function makes the method numerically stable especially when the number of predictors far exceeds the number of the observations. A fast coordinate descent algorithm is exploited to avoid matrix operations and speed up the computation. This is joint work with Tongtong Wu from the University of Maryland.
The second method is pathway-based index model. Motivated by the concept of personalized medicine, the proposed hierarchical framework models a survival related phenotype as attributable to known genes within known biological pathways. Given genes identified as conferring increased or decreased risk within a pathway, a pathway summary, or index, is then constructed. The indices are used in a second model to identify important pathways and predict future patients' survival. Using TCGA data, we show that the patient-specific index scores across important pathways (referred to as patient-specific risk profiles) are powerful and efficient characterizations useful in addressing a number of questions related to predicting survival and optimizing treatment. This is joint work with Kevin Eng and Christina Kendziorski from the University of Wisconsin, Madison.
Dr. Sijian Wang received his Ph.D. in Biostatistics from the University of Michigan in 2008. He is now an assistant professor in the department of Biostatistics and Medical Informatics and the Department of Statistics. Dr. Wang is interested in high-dimensional data analysis, machine learning, survival analysis, longitudinal data analysis, missing data and statistical modeling in medical sciences.
|Date:||Thursday, April 21, 2011|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York