Sherri Rose has been honored with the prestigious 2024 Outstanding Statistical Application Award from the American Statistical Association for research that developed a novel class of statistical estimators to evaluate the impact of Medicaid managed care plans.

Rose, PhD, a professor of health policy and director of Stanford’s Health Policy Data Science Lab, received the award at the Joint Statistical Meetings in Portland, Oregon earlier this month alongside her co-authors of the award-winning paper published in Biometrics. This is the second major award for the manuscript, titled “Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaid,” which also received the ASHEcon Manning Award for Best Research in Health Econometrics in June.

Rose and co-authors—Jacob Wallace (Yale), Timothy Layton (Harvard), and Health Policy Data Science Lab alumna and first author Irina Degtiar (Mathematica)—found that when they applied their new algorithms, there were substantial differences in health-care spending by individual Medicaid managed care plans. 

“Applying these methods, we find substantial heterogeneity in spending effects across managed care plans,” they wrote. “This has major implications for our understanding of Medicaid, where this heterogeneity has previously been hidden. Additionally, we demonstrate that unmeasured confounding rather than lack of overlap poses a larger concern in this setting.”

Founded in 1839, the American Statistical Association is the world’s largest community of statisticians and the second oldest continuously operating professional association in the United States. The annual Outstanding Statistical Application Award recognizes the authors of a paper that demonstrates an outstanding application of statistics in any substantive field.