Biostatistics Seminar Series with Sherri Rose, Ph.D.

February 24


This event is hosted by the Department of Biostatistics.

PRESENTER: Sherri Rose, Ph.D. Stanford University

BIO: Sherri Rose, Ph.D. is an Associate Professor at Stanford University in the Center for Health Policy and Center for Primary Care and Outcomes Research. She is also Co-Director of the Health Policy Data Science Lab. Her methodological research focuses on machine learning for prediction and causal inference. Within health policy, Dr. Rose works on risk adjustment, algorithmic fairness, comparative effectiveness, and health program evaluation. She was recently named a fellow of the American Statistical Association and her other honors include the ISPOR Bernie J. O’Brien New Investigator Award, an NIH Director’s New Innovator Award, and Mid-Career Awards from the American Statistical Association and Penn-Rutgers Center for Causal Inference.

ABSTRACT: Health care is moving toward analytic systems that take large databases and estimate varying quantities of interest, incorporating advances from statistics, econometrics, and computer science. The massive size of the health care sector makes data science applications in this space particularly salient for social policy. This presentation will discuss specific challenges related to developing and deploying statistical machine learning algorithms for health economics and outcomes research. Considerations go beyond typical measures of statistical assessment, and include concepts such as dataset shift and algorithmic fairness. An overarching theme is the critical need to center marginalized populations in developing methodology for health care given the stakes involved.