Zhongyuan Chen, a Visiting Assistant Professor in the Department of Statistics at Purdue University and candidate for a faculty position in the Division of Biostatistics and the Masonic Institute for the Developing Brain, will present:
“Data-guided Statistical Methods for Treatment Recommendations, Cancer Association Studies, and Beyond”
Abstract: Despite the availability of large amounts of genomic-clinical data, medical treatment recommendations have yet to successfully take good advantage of them. In this talk, I will first introduce a data-guided statistical machine learning approach for treatment recommendation with feature scores by applying a dimension reduction method (Sliced Inverse Regression). It allows highly general regression models for the treatment response, a large number of covariates, and convenient visualization of the optimal treatment recommendation. We derive the consistency and the convergence rate of the proposed approach and demonstrate its effectiveness using simulation studies and a real-data example of the treatment of multiple myeloma. I will also show that, for some data, the optimal treatment recommendation can be achieved by accurate estimation of the conditional average treatment effect. This study further sheds light on a practical guide on treatment recommendations using treatment effect estimation.
Next, I will discuss data-guided statistical methods in cancer association studies between somatic mutations and germline variations, which is important for cancer risk prediction. We apply data-adaptive and pathway-based statistical tests to a large-scale real-world International Cancer Genome Consortium (ICGC) whole genome sequencing dataset. A low-rank approximation method is adopted to preselect parameters to improve the efficiency of the algorithm. I will also briefly discuss molecular characterization and clinical relevance of metabolic expression subtypes in human cancers using molecular data of 9,125 patient samples from The Cancer Genome Atlas (TCGA).
The presentation will be mainly based on joint work with collaborators at Purdue University and MD Anderson Cancer Center.
All are Welcome.