December 9, 2021 Webinar
Major Statistical Challenges in Count Data Analysis
Hui Zhang, PhD
Abstract
Count data plays an important role in biomedical and clinical research, especially nowadays with the rapid biomedical technique progresses including next generation sequencing. However, using routine statistical model to analyze counts, such as Poisson model, often results in biased or even misleading conclusions. Primary challenges when applying Poisson model to count data include over-dispersion and zero-inflation, which are commonly encountered in practice. In addition, the repeated measures and incomplete observations in modern clinical trials and survey studies add to complexity. This talk will review these challenges in count data analysis and recent methodology progresses made by the speaker's group. In addition, the application of counting process method to the cutting-edge single-molecule localization microscopy image analysis and recently developed innovative statistical methods will also be introduced.
Short Bio
Dr. Hui Zhang is Professor of Preventive Medicine (Biostatistics) at Northwestern University Feinberg School of Medicine. He obtained his Bachelor degree from Nankai University, China, and Ph.D. degree in Statistics from University of Rochester. After his graduation in 2010, he joined St. Jude Children's Research Hospital as Assistant Member and then promoted to Associate Member. In 2019, he joined the Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine as a professor. His research interest includes categorical data, longitudinal data and missing data.