Health Policy Data Science Lab
We are a group of interdisciplinary researchers at Stanford led by Dr. Sherri Rose. We develop and apply quantitative methods to solve problems in health policy, leveraging techniques from statistics, computer science, economics, epidemiology, and decision science.
Our areas of scientific interest are broad, spanning trade-offs for multiple health outcomes, causal inference, computational health economics, comparative effectiveness research, and payment reform impact evaluation.
What unites the Lab is an emphasis on pre-specified and transparent methodological choices, sound statistical decision making, data-motivated methodology development in the area of health policy, and effective presentation of results.
The Lab was originally co-founded in 2015 by Dr. Laura Hatfield and Dr. Sherri Rose.
Upcoming Event
Click to register for the Post-PhD Health Data Science Careers Panel
Selected Recent Publications
Agata Foryciarz, Nicole Gladish, David H. Rehkopf, Sherri Rose (2024). Incorporating Area-Level Social Drivers of Health in Predictive Algorithms Using Electronic Health Record Data. [Preprint]
Oana Enache, Lisa Goldman Rosas, Sherri Rose (2024). Clinical Research Reporting Paradigms May Misrepresent Participant Identities. American Journal of Epidemiology. [Link] [SHP News]
Marika Cusick, Glenn Chertow, Douglas Owens, Michelle Williams, Sherri Rose (2024). Algorithmic Changes Are Not Enough: Evaluating the Removal of Race Adjustment From the eGFR Equation. CHIL Conference. [Link] [SHP News] [Policy Brief]
Michelle Mello, Sherri Rose (2024). Denial—Artificial Intelligence Tools and Health Insurance Coverage Decisions. JAMA Health Forum. [Link]
Gary Collins, Karel Moons, Paula Dhiman, Richard Riley, Andrew Beam, Ben Van Calster, Marzyeh Ghassemi, Xiaoxuan Liu, Johannes Reitsma, Maarten van Smeden, Anne-Laure Boulesteix, Jennifer Camaradou, Leo Celi, Spiros Denaxas, Alastair Denniston, Ben Glocker, Robert Golub, Hugh Harvey, Georg Heinze, Michael Hoffman, André Pascal Kengne, Emily Lam, Naomi Lee, Elizabeth Loder, Lena Maier-Hein, Bilal Mateen, Melissa McCradden, Lauren Oakden-Rayner, Johan Ordish, Richard Parnell, Sherri Rose, Karandeep Singh, Laure Wynants, Patricia Logullo (2024). TRIPOD+AI Statement: Updated Guidance for Reporting Clinical Prediction Models That Use Regression or Machine Learning Methods. BMJ. [Link]
Irina Degtiar, Tim Layton, Jacob Wallace, Sherri Rose (2023). Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaid. Biometrics. [Link] [ASHEcon Award] [ASA Award]