Queensland Branch Meeting: Prof Munni Begum

Please join us online for the August Queensland Branch Meeting!

When: 11:00 AM - 12:00 PM (AEST)

Wednesday 25th August 2021

Location: Zoom

Subgroup Analysis and Identification of Biologically Meaningful Predictive Biomarkers

Abstract: Subgroup analysis concerns with identifying differential treatment effects for a group of individuals with certain health conditions. A treatment may be more effective for a particular group of subjects, and less effective for another group of subjects based on their demographic characteristics, and other risk factors. The basis of subgroup analysis is to identify a group of subjects for whom the treatment has an enhanced effect against hard-to-treat diseases such as, cancer. This presentation has two parts: First, I will briefly illustrate subgroup analysis with differential effect of cardiorespiratory fitness (CRF) on all-cause, cardiovascular, and cancer mortality; Second, I will outline my current research project on identifying biologically meaningful predictive biomarkers as an extended subgroup analysis

About the Speaker:

Professor Munni Begum is a Professor of Statistics in the Department of Mathematical Sciences at Ball State University, Muncie, IN, USA. She started her journey in the field of statistics at the University of Dhaka, Bangladesh, where she obtained a Bachelor of Science with honor and a Master of Science in statistics. She came to the USA to pursue higher studies in biostatistics and earned a Master of Arts in mathematical statistics from Ball State University and a Doctor of Public Health in biostatistics from the University of North Carolina at Chapel Hill. She was the advisor of the statistics graduate program from 2014 – 2017 and supervised more than thirty Master theses. Dr. Begum initiated the Master of Science in Data Science program at Ball State University that has just launched in Fall 2020. Her research interests are in developing statistical and data science methods with applications to biomedical and health science fields. In particular, she has keen interests in big data, predictive modeling, models for correlated responses, multivariate techniques, Bayesian methods and hierarchical modeling, and survival analysis.