Statisticians Leading the Way: Leveraging the All of Us Research Program Data to Advance Science

Music City Center 

Room: CC-214 

Large-scale biobank initiatives are becoming a cornerstone of scientific research worldwide. Initiatives like the UK Biobank have demonstrated the transformative potential of this global trend, driving scientific discoveries and resulting in high-impact publications. Building on this momentum, the U.S. NIH's All of Us Research Program is a historic effort to advance precision medicine by enrolling over one million participants nationwide. As one of the largest and most diverse biobanks of its kind, All of Us provides a rich dataset that integrates multiple data domains, including surveys, electronic health records (EHR), genomics, and Fitbit data.

For statisticians, the richness of the All of Us data provides unique opportunities for statistical research, including time-dependent analysis, causal inference, statistical machine learning, and integrative analysis of large genetic, epidemiological, and EHR data. Therefore, as All of Us continues to expand and evolve, the statistical community should be ready to seize these forthcoming opportunities. Ultimately, learning how to leverage this resource will position statisticians to not only advance statistical methodology, but also take the lead in driving scientific breakthroughs.

This session will provide an overview of the All of Us data and discuss its analytic opportunities and challenges. It aims to engage statisticians in exploring how this rich dataset can be leveraged to drive innovative statistical methods and applications that advance scientific discovery. In addition, this session will provide insight on how biobanks are shaping the future of statistics and data science.

This panel will feature leading experts with diverse perspectives.

-Xihong Lin is Professor of Biostatics and Statistics at Harvard. She is a member of the National Academy of Medicine and National Academy of Sciences. She is an expert on developing novel, scalable statistical and ML methods for large-scale genetic and biobank data.
-Michael Boehnke is Richard G. Cornell Distinguished University Professor of Biostatistics at the University of Michigan (UM) and Director of the UM Center for Statistical Genetics. He is a member of the National Academy of Medicine and an expert on the design, statistical analysis, and novel methodology development for large-scale genetic studies.
-Bhramar Mukherjee is Anna M.R Lauder Professor of Biostatistics, Epidemiology, Statistics and Data Science, and Senior Associate Dean of Public Health Data Science and Data Equity at Yale. She is a member of the National Academy of Medicine and an expert on developing novel statistical methods for epidemiological, EHR, and genomic data from large-scale biobanks.
-Paul Harris is Professor of Biomedical Informatics and Biostatistics at Vanderbilt. He is the contact-PI of the All of Us Research Program's Data and Research Center and is an expert on developing novel informatics methods that facilitate the collection and management of data.
-Hua Xu is Robert T. McCluskey Professor of Biomedical Informatics and Data Science and Assistant Dean for Biomedical Informatics at Yale. He is an expert on developing novel statistical ML and natural language processing methods for analyzing large-scale EHR and biobank data.
-Qingxia "Cindy" Chen is Professor and Vice Chair of Education of Biostatistics at Vanderbilt. She is an expert on developing novel statistical methods for EHR and genetics data from large-scale biobanks.

Yes

Section on Statistics in Genomics and Genetics

Section on Statistical Learning and Data Science

Section on Statistics and Data Science Education