Statistical Methods and Data Science

Division of Biostatistics

Current methods development is done within the context of the IMAGE Program Project: The overall goal of this research program is the integration of different types of information to build comprehensive statistical models for cancer causes and prognosis. Motivated by various studies of colorectal cancer, we are developing novel statistical methods that use prior biological knowledge to inform integrative genomics analyses, that use phylogenetic information to infer gene function, that incorporate high-dimensional data on the internal environment (the microbiome and the exposome), and that model tumor evolution from data on somatic changes within and between tumors. Beyond colon cancer, our methods are broadly applicable to other cancer types and many other chronic diseases.

METHODS DEVELOPMENT

Current Topics

Collaborators

in Statistical Methods Development

Juan Pablo Lewinger, PhD

Assistant Professor of Population and Public Health Sciences

Duncan Thomas, PhD

Professor of Population and Public Health Sciences

Nick Mancuso, PhD

Assistant Professor of Population and Public Health Sciences

William Gauderman, PhD

Chief, Division of Biostatistics

Joshua Millstein, PhD

Assistant Professor of Population and Public Health Sciences

READ OUR WORK

Recent Publications

ENVIRON INT
Perfluoroalkyl substances and severity of nonalcoholic fatty liver in Children: An untargeted metabolomics approach

NAT COMM
An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

CANCER MED
Functional informed genome-wide interaction analysis of body mass index, diabetes and colorectal cancer risk