Innovative Statistical Approaches to address Emerging Challenges in Public Health
Category: International Statistical Institute
The COVID-19 pandemic has highlighted a multitude of emerging statistical challenges in Public Health which are unique and distinct from other areas in health sciences with areas such as environmental epidemiology having detailed measurements on persistent organic pollutants that depict ultra-high correlation/inter-dependence whereas other areas only having spatially aggregated data at locality (zip code, census tract) level. Integration of meta-data from multiple levels, such as individual and aggregated, or from multiple entities, brings in a host of methodological challenges. In the spirit of ISI2023 theme of Statistical Science for a better world, this session will include four internationally renowned researchers from South Africa and the United States who have committed to present their innovations at the Congress.
Dr. Somnath Datta, Professor and Preeminence Hire in Genomic Medicine, Department
of Biostatistics, University of Florida, Gainesville, Elected Member of International Statistical Institute, Elected Fellow of American Statistical Association, and Elected Fellow of Institute of Mathematical Statistics, Past President of International Indian Statistical Association, Editor-in-Chief, Frontiers in Statistical Sciences and Probability, Past Co-Editor-in-Chief, Statistics & Probability Letters, Dr. Datta is a renowned expert in statistical methodologies for time-to-event data and has developed novel statistical methods for analyzing public health, dental and biomedical data.
Dr. Freedom Gumedze, Associate Professor and Head of the Department of Statistical Sciences, University of Cape Town, South Africa, currently serving on the executive board of the International Biometric Society and advisory board of the National Graduate Academy for Mathematical and Statistical Sciences in South Africa. Dr, Gumedze is an established researcher in statistical methods in health sciences research and has expertise in detection of outliers/robust estimation, survival analysis, joint analysis of longitudinal data and survival data, competing or semi-competing risks,
Dr. Ananda Sen, Lee A. Green Collegiate Research Professor, Department of Biostatistics and Department of Family Medicine, School of Public Health, University of Michigan, Ann Arbor. Elected Member of International Statistical Institute, Elected Fellow of the American Statistical Association. Dr. Sen is a renowned expert in analysis of competing risks and multi-state survival models, Bayesian models and methods in biomedical applications and has been recognized for biostatistical contributions to areas in women’s health, cancer prevention, and quality of life.
Dr. Sanjib Basu, the session organizer, is the Paul Levy and Virginia F. Tomasek Professor of Biostatistics, Head, Section of Biostatistics, Director, Center for Biostatistical Development, School of Public Health, University of Illinois Chicago, He leads the Population Health Analytics Metrics and Evaluation center whose mission is to democratize data for population health. He is an Elected member of the International Statistical Institute, Elected Fellow of the American Statistical Association, Past President of International Indian Statistical Association, and currently serving in Editorial boards of 4 high-impact statistics journals. Dr. Basu is a renowned and established researcher in Bayesian methods, innovative Bayesian models, competing risks and cure rate and statistical methodologies in Public Health and Environmental mixtures.
Overall, the four presentations will highlight the innovations of methodologies in emerging public health issues that will be of interest to researcher as well as practitioners.
- A high-dimensional joint model in public health context
- A joint model of longitudinal data and survival data with detection and downweighting of longitudinal outliers
- Bayesian Joint Modeling under Competing Risks with Application in Cancer
- Specialized Statistical Analyses of Iowa Fluoride Study Data