Faculty and Staff Cancer Biostatistics

Heidi L. Weiss, PhD

Professor, Division Chief

Expertise:

• Bayesian and adaptive designs and interim analysis for early phase oncology clinical trials
• Experimental designs for in vivo mouse models in cancer
• Biomarker studies in translational human samples

Brent J. Shelton, PhD

Professor

Expertise:

• Statistical planning, design and analysis of cancer-relevant behavioral intervention studies
• Design and analysis of cluster-randomized and stepped-wedge trials
• Mediation-moderation analyses
• Statistical methods for missing data
• General biostatistical methods and applications
• Future area of interest: Incorporation of social network analyses to inform cluster composition in cluster randomized trials

Chi Wang, PhD

Professor

Expertise:

• Cancer driver mutation identification and interpretation
• Statistical methods for transcriptomic and metabolomic data
• Prognostic and predictive modeling based on high-dimensional omics data

Bin Huang, DrPH

Professor

Expertise:

• Population-based cancer outcome research through utilizing cancer registry data and other secondary data
• Linking and utilization of Health administrative claims data
• Study design and analysis of community trials

Li Chen, PhD

Associate Professor

Expertise:

• Survival analysis
• Biomarker identification and evaluation
• Analysis of incomplete data

Donglin Yan, PhD

Assistant Professor

Expertise:

• Dose-escalation designs for early phase oncology clinical trials
• Principle component analysis for flow-cytometry data
• Quality of life data analysis
• Applied statistics in clinical trials

Jinpeng Liu, PhD

Assistant Professor

Expertise:

• Multi-Omics data process, analysis, and integration for biomarker identification
• Computational algorithms development for single cell transcriptomics data
• Bioinformatics platform design and implementation for big data analytics

Feitong Lei, PhD

Assistant Professor

Expertise:

• Population-based cancer research, focusing on epidemiological patterns and risk factors
• Applied biostatistical methods, tailored for real-world data analysis
• Study design and analysis using diverse data sources, such as the cancer registry, administrative claims, and prescription drug monitoring program data.