The Division of Public Health Sciences (PHS) at Wake Forest University School of Medicine identifies strategies to enhance public health and prevent disease. PHS is a group of public health-oriented faculty and staff whose activities focus on developing better strategies for maintaining the health of the entire community.
Public Health Sciences is committed to recognizing, uplifting and supporting all communities in our work and in our daily lives.
Turning data into knowledge to improve the health of everyone everywhere.
The Department of Epidemiology and Prevention is dedicated to research and teaching in the etiology and prevention of chronic diseases and their complications
The Department of Implementation Science is the catalyst for the adoption, integration and sustainability of evidence-based practices into real world settings to improve population health.
The Department of Social Sciences and Health Policy aims to improve the human condition through the application of social sciences and policy research to health and health care.
Our faculty is focused on developing better strategies for maintaining the health of the entire community.
Director, Public Health Sciences
Professor, Public Health Sciences (Winston-Salem)
Interim Chair, Biostatistics and Data Science
Associate Professor, Biostatistics and Data Science, Public Health Sciences (Winston-Salem)
Professor, Public Health Sciences (Winston-Salem)
Chair, Implementation Science
Professor, Implementation Science, Public Health Sciences (Winston-Salem)
Chair, Epidemiology and Prevention
Professor, Epidemiology and Prevention, Public Health Sciences (Winston-Salem)
Chair, Social Sciences and Health Policy, Public Health Sciences (Winston-Salem)
Professor, Social Sciences and Health Policy, Public Health Sciences (Winston-Salem)
Professor Emeritus, Public Health Sciences
Associate Director, Public Health Sciences
Professor, Social Sciences and Health Policy, Public Health Sciences (Winston-Salem)
The focus of Dr. Speiser’s work is conducting benchmarking studies to compare statistical and machine learning methods using many real-world datasets. The results are aggregated to make data-driven recommendations for optimal methodology in efforts to improve rigor in clinical and translational research. The first benchmarking study, funded by a KL2 grant from the Wake Forest CTSI, focused on comparing random forest variable selection methods for categorical outcomes and has been cited nearly 1000 times (PMID32968335). The team is currently working on a similar study comparing random forest variable selection methods for continuous outcomes, funded by a pilot grant from the Wake Forest CTSI Biostatistics Epidemiology & Research Design Program. These tools are used with longitudinal, repeated measures and include applications in aging that use predictors and outcomes in older adults collected from year to year. The team is developing an R package to share code for processing and harmonizing longitudinal data from studies of aging, as well as synthetic datasets derived from these studies to facilitate benchmarking analyses. The photo is from the Joint Statistical Meetings in August 2022 for an invited session (and packed house!) entitled Practical Recommendations for Prediction Modeling that Advance Innovation with speakers Jaime Speiser, Nate O’Connell, Byron Jaeger, and Joe Rigdon.
Collaborators: Jaime Speiser, Mike Miller, Eddie Ip, Janet Tooze, Nate O’Connell, Byron Jaeger, Garrett Bullock, all BDS; Kate Callahan and Denise Houston, Gerontology and Geriatric Medicine; and David Miller, Implementation Science.