SELECTED PUBLICATIONS

Recent commentaries on ethical AI: JAMA Health Forum, Nature Machine Intelligence, npj Digital Medicine, Biometrics

Recent commentaries on minimum standards for machine learning in health: BMJ, JAMIA, Nature Medicine

Fair Risk Adjustment

Policy Brief: A. Zink, T. McGuire, S. Rose (2022). Balancing fairness and efficiency in health plan payments, Stanford HAI Policy Briefs. [Link]

A. Zink, S. Rose (2021). Identifying undercompensated groups defined by multiple attributes in risk adjustment, BMJ Health & Care Informatics. [Link][Code]

T. McGuire, A. Zink, S. Rose (2021). Improving the performance of risk adjustment systems: Constrained regressions, reinsurance, and variable selection, American Journal of Health Economics. [Link][NBER Version][Code][Stanford Health Policy News: Improving the Performance of Health Plan Payment Systems]

A. Zink, S. Rose (2020). Fair regression for health care spending, Biometrics. [Link][Code][Top Cited Article 2020-2021 in Biometrics][Top Downloaded Article 2020-2021 in Biometrics]

S. Bergquist, T. Layton, T. McGuire, S. Rose (2019). Data transformations to improve the performance of health plan payment methods, Journal of Health Economics. [Link]

S. Rose (2018). Robust machine learning variable importance analyses of medical conditions for health care spending, Health Services Research. [PDF][Finalist, 2019 NIHCM Health Care Research Award, Top Downloaded Article 2017-18 in Health Services Research]

S. Rose, S. Bergquist, T. Layton (2017). Computational health economics for identification of unprofitable health care enrollees, Biostatistics. [Link][Code][HMS News: Deep Dive]

S. Rose, A. Zaslavsky, J.M. McWilliams (2016). Variation in accountable care organization spending and sensitivity to risk adjustment: Implications for benchmarking, Health Affairs. [Link][Featured in official letter to CMS signed by 22 health organizations, AMGA letter][Discussed by Health Affairs Blog, HealthExec, CHSR, The Source]

S. Rose (2016). A machine learning framework for plan payment risk adjustment, Health Services Research. [Link][Discussed by The Conversation, AHE Blog]

Causal Methods (See Also: BOOKS)

I. Degtiar, T. Layton, J. Wallace, S. Rose (2023). Conditional cross-design synthesis estimators for generalizability in Medicaid, Biometrics. [Link][arXiv Version][Code][Stanford Health Policy News: ASHEcon Award, ASA Award]

M. van der Laan, S. Rose (2023). Why machine learning cannot ignore maximum likelihood estimation. [Book Chapter Version][arXiv Version]

Editorial: S. Rose, D. Rizopoulos (2020). Machine learning for causal inference in Biostatistics. [Link]

S. Adhikari, S. Rose, S.L. Normand (2020). Nonparametric Bayesian instrumental variable analysis: Evaluating heterogeneous effects of coronary arterial access site strategies, Journal of the American Statistical Association. [Link]

S. Rose, S.L. Normand (2019). Double robust estimation for multiple unordered treatments and clustered observations: Evaluating drug-eluting coronary artery stents, Biometrics. [PDF][Top Downloaded Article 2018-19 in Biometrics]

Reviews & Tutorials

I. Degtiar, S. Rose (2023). A review of generalizability and transportability, Annual Review of Statistics and Its Application. [Link] [arXiv Version]

I. Chen, E. Pierson, S. Rose, S. Joshi, K. Ferryman, M. Ghassemi (2021). Ethical machine learning in health care, Annual Review of Biomedical Data Science. [Link][arXiv Version]

S. Rose (2020). Intersections of machine learning and epidemiological methods for health services research, International Journal of Epidemiology. [Link]

T. Blakely, J. Lynch, K. Simons, R. Bentley, S. Rose (2020). Reflection on modern methods: When worlds collide — prediction, machine learning, and causal inference, International Journal of Epidemiology. [Link][The Best of IJE 2019 Most Read Article]

R. Ellis, B. Martins, S. Rose (2018). Risk adjustment for health plan payment. In McGuire, van Kleef, eds. Risk Adjustment, Risk Sharing and Premium Regulation in Health Insurance Markets: Theory and Practice. [Link][Preprint Version]

M. Schuler, S. Rose (2017). Targeted maximum likelihood estimation for causal inference in observational studies, American Journal of Epidemiology. [Link][2017 AJE Article of the Year] 

J. Snowden, S. Rose, K. Mortimer (2011). Implementation of G-Computation on a simulated data set: demonstration of a causal inference technique, American Journal of Epidemiology. [PDF][with Discussion and Invited Reply][Evaluated by Faculty of 1000]

Machine Learning for Classification

S. Bergquist, G. Brooks, M. Landrum, N. Keating, S. Rose (2022). Uncertainty in lung cancer stage for outcome estimation via set-valued classification, Statistics in Medicine. [Link][Code]

S. Adhikari, S.L. Normand, J. Bloom, D. Shahian, S. Rose (2021). Revisiting performance metrics for prediction with rare outcomes, Statistical Methods in Medical Research. [Link][Accepted Version][Code]

S. Rose (2018). Machine learning for prediction in electronic health data, JAMA Network Open. [Link][Press coverage in Politico]

A.J. Rosellini, F. Dussaillant, J. Zubizarreta, R. Kessler, S. Rose (2018). Predicting posttraumatic stress disorder following a natural disaster, Journal of Psychiatric Research. [Link][Press coverage in Psychology Today]

S. Bergquist, G. Brooks, N. Keating, M.B. Landrum, S. Rose (2017). Classifying lung cancer severity with ensemble machine learning in health care claims data, Proceedings of Machine Learning Research. [PDF]

R. Kessler, C. Warner, C. Ivany, M. Petukhova, S. Rose, et al. (2015). Predicting suicides after psychiatric hospitalization in US Army soldiers, JAMA Psychiatry. [Link][Press coverage in The New York Times, USA Today, US News, Los Angeles Times]

S. Rose (2013). Mortality risk score prediction in an elderly population using machine learning, American Journal of Epidemiology. [PDF]["Editor's Choice"][Press coverage in Slate]

Program & Policy Evaluation

M. Cusick, G. Chertow, D. Owens, M. Williams, S. Rose (2024). Algorithmic Changes Are Not Enough: Evaluating the Removal of Race Adjustment from the eGFR Equation, Proceedings of the Conference on Health, Inference, and Learning. [Link][Code][Press Coverage in Nature][Stanford Impact Labs: How Will the Evaluation of Algorithms Lead to More Equitable Health Outcomes?][Stanford Health Policy News: Removing Race Adjustment in Chronic Kidney Disease Care]

A. McDowell, J. Raifman, A. Progovac, S. Rose (2020). Association of nondiscrimination policies with mental health among gender minority individuals, JAMA Psychiatry. [Link][HMS News: Lifesaving Protections][Press Coverage in Scientific American, The Hill, Huffington Post, Tradeoffs Podcast]

C. Carroll, M. Chernew, A.M. Fendrick, J. Thompson, S. Rose (2018). Effects of episode-based payment on health care spending and utilization: Evidence from perinatal care in Arkansas, Journal of Health Economics. [Link][Featured in NBER Bulletin]

F. Mateen, E. McKenzie, S. Rose (2018). Medical schools in fragile states: Implications for delivery of care, Health Services Research. [Link][with Discussion]

J. Spertus, S.L. Normand, R. Wolf, M. Cioffi, A. Lovett, S. Rose (2016). Assessing hospital performance after percutaneous coronary intervention using big data, Circulation: Cardiovascular Quality and Outcomes. [Link]

Z. Song, S. Rose, D. Safran, B. Landon, M. Day, M. Chernew (2014). Changes in health care spending and quality 4 years into global payment, New England Journal of Medicine. [Link][Editorial in NEJM][Press coverage in The New York Times, US News, The Boston Globe]

Informatics

M. Majumder, M. Cusick, S. Rose (2023). Measuring concordance of data sources used for infectious disease research in the USA: A retrospective data analysis, BMJ Open. [Link][medRxiv Version]

M. Majumder, S. Rose (2021). A generalizable data assembly algorithm for infectious disease outbreaks, JAMIA Open. [Link][medRxiv Version][Code]

D. Carrell, R. Schoen, D. Leffler, M. Morris, S. Rose, et al. (2017). Challenges in adapting existing clinical natural language processing systems to multiple diverse healthcare settings, Journal of the American Medical Informatics Association. [Link] 

Telemedicine

L. Uscher-Pines, L. Riedel, A. Mehrotra, S. Rose, A. Busch, H. Huskamp (2023). Many clinicians implement digital equity strategies to treat opioid use disorder, Health Affairs. [Link]

S. Patel, S. Rose, M. Barnett, H. Huskamp, L. Uscher-Pines, A. Mehrotra (2021). Community factors associated with telemedicine use during the COVID-19 pandemic, JAMA Network Open. [Link]

A. McDowell, H. Huskamp, A. Busch, A. Mehrotra, S. Rose (2021). Patterns of mental health care before initiation of telemental health services, Medical Care. [Link]

A. Wilcock, S. Rose, A. Busch, H. Huskamp, L. Uscher-Pines, B. Landon, A. Mehrotra (2019). Association between broadband internet availability and telemedicine use, JAMA Internal Medicine. [Link][Press Coverage in US News]

H. Huskamp, A. Busch, J. Souza, L. Uscher-Pines, S. Rose, et al. (2018). How is telemedicine being used for opioid and other substance use disorder treatment? Health Affairs. [Link][Press Coverage in PBS News Hour]

A. Mehrotra, H. Huskamp, J. Souza, L. Uscher-Pines, S. Rose, et al. (2017). Rapid growth in mental health telemedicine use among rural Medicare beneficiaries, wide variation across states, Health Affairs. [Link]