Academic Leadership Award: Training Program in Gerontologic Biostatistics and Data Science - The 2020 US Census reported that the older adult population (aged ≥65 years) has reached a historical absolute (55.8 million persons) and relative (16.8% of U.S. population) peak. Older adults are also the segment of the population that has experienced the fastest 10-year growth. However, 70% of older adult fee-for-service Medicare beneficiaries have multiple (≥2) comorbid conditions, which are responsible for 94% of the $750 billion in annual Medicare spending. Thus, older adults’ gains in healthspan have not kept pace with their gains in lifespan. The NIA’s strategic directions for improving healthspan and longevity include goals to increase capacity among the workforce in aging research: (1) training the workforce for rigorous research on aging and (2) supporting development of technologies and methods to enhance analysis and sharing of datasets to promote interdisciplinary aging research. As a leader in the field of gerontologic biostatistics and data science at the University of Maryland School of Medicine within the University of Maryland Baltimore (UMB), I have a strong track record of mentoring and research in aging. Thus, I propose to develop and direct the Training Program in Gerontologic Biostatistics and Data Science to enhance trainees’ (students, postdoctoral fellows, junior faculty) capacity in aging research and fulfill the NIA’s goals. The term gerontologic biostatistics and data science describes statistical methods that address the challenging features of modern aging research data, including but not limited to, longitudinal outcomes truncated by death, misclassification and bias due to proxy respondents, interpretation of signal-intensive measures (e.g., from brain (f)MRI, wearable devices), and analysis of high-dimensional multi-omic data (e.g., microbiome, metabolome, proteome). Due to these rapidly changing data complexities, biostatistics/data science is often a rate-limiting skillset to advancing impactful aging research. To overcome this barrier, the Academic Leadership Award will provide crucial protected time for me to develop and direct a comprehensive, sustainable, and scalable training program in gerontologic biostatistics and data science (GBDS) comprising a didactive curriculum (Aim 1) that can lead to a prestigious credential for degree-seeking trainees, mentoring of trainees in GBDS for grant writing of career development (e.g., NIH F, K) proposals (Aim 2), and a workshop series on how to access and analyze publicly available secondary data resources (Aim 3) such as the NIA Aging Research Biobank. The program is comprehensive in the types of data and skills it covers, is sustainable due to integration with UMB’s strong existing education and research infrastructure in aging that supports its designation as an Age Friendly University, and is scalable owing to plans to create digital training materials (recorded lectures, R code, tutorials) to be made publicly available on national platforms facilitated by strategic stakeholder partnerships and by implementing the principles of data findability, accessibility, interoperability, and reusability. The ultimate impact is enhanced capacity of academic research in gerontology, geriatrics, and geroscience at UMB and beyond to extend the lifespans and healthspans of older adults.
