SELECTED PUBLICATIONS
Fair Risk Adjustment
Reitsma, McGuire, Rose (2025). Algorithms to improve fairness in Medicare risk adjustment. [Preprint][Code]
Policy Brief: Zink, McGuire, Rose (2022). Balancing fairness and efficiency in health plan payments, Stanford HAI Policy Briefs. [Link]
Zink, Rose (2021). Identifying undercompensated groups defined by multiple attributes in risk adjustment, BMJ Health & Care Informatics. [Link][Code]
McGuire, Zink, 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]
Zink, 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]
Bergquist, Layton, McGuire, Rose (2019). Data transformations to improve the performance of health plan payment methods, Journal of Health Economics. [Link]
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]
Rose, Bergquist, Layton (2017). Computational health economics for identification of unprofitable health care enrollees, Biostatistics. [Link][Code][HMS News: Deep Dive]
Rose, Zaslavsky, 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]
Rose (2016). A machine learning framework for plan payment risk adjustment, Health Services Research. [Link][Discussed by The Conversation, AHE Blog]
Algorithmic Impact
Cusick, Alarid-Escudero, Goldhaber-Fiebert, Rose (2025). A novel decision modeling framework for health policy analyses when outcomes are influenced by social and disease processes. [Preprint][Code]
Cusick, Chertow, Owens, Williams, 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]
Mello, Rose (2024). Denial—Artificial intelligence tools and health insurance coverage decisions, JAMA Health Forum. [Link]
Causal Methods (See Also: BOOKS)
Degtiar, Layton, Wallace, Rose (2023). Conditional cross-design synthesis estimators for generalizability in Medicaid, Biometrics. [Link][arXiv Version][Code][Stanford Health Policy News: ASHEcon Award, ASA Award]
van der Laan, Rose (2023). Why machine learning cannot ignore maximum likelihood estimation. [Book Chapter Version][arXiv Version]
Editorial: Rose, Rizopoulos (2020). Machine learning for causal inference in Biostatistics. [Link]
Adhikari, Rose, Normand (2020). Nonparametric Bayesian instrumental variable analysis: Evaluating heterogeneous effects of coronary arterial access site strategies, Journal of the American Statistical Association. [Link]
Rose, 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
Degtiar, Rose (2023). A review of generalizability and transportability, Annual Review of Statistics and Its Application. [Link] [arXiv Version]
Chen, Pierson, Rose, Joshi, Ferryman, Ghassemi (2021). Ethical machine learning in health care, Annual Review of Biomedical Data Science. [Link][arXiv Version]
Rose (2020). Intersections of machine learning and epidemiological methods for health services research, International Journal of Epidemiology. [Link]
Blakely, Lynch, Simons, Bentley, 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]
Ellis, Martins, 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]
Schuler, Rose (2017). Targeted maximum likelihood estimation for causal inference in observational studies, American Journal of Epidemiology. [Link][2017 AJE Article of the Year]
Snowden, Rose, 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]
Prediction & Classification
Foryciarz, Gladish, Rehkopf, Rose (2025). Incorporating area-level social drivers of health in predictive algorithms using electronic health records, JAMIA. [Link][Code]
S. Bergquist, G. Brooks, M. Landrum, N. Keating, Rose (2022). Uncertainty in lung cancer stage for outcome estimation via set-valued classification, Statistics in Medicine. [Link][Code]
Adhikari, Normand, Bloom, Shahian, Rose (2021). Revisiting performance metrics for prediction with rare outcomes, Statistical Methods in Medical Research. [Link][Accepted Version][Code]
Rosellini, Dussaillant, Zubizarreta, Kessler, Rose (2018). Predicting posttraumatic stress disorder following a natural disaster, Journal of Psychiatric Research. [Link][Press coverage in Psychology Today]
Bergquist, Brooks, Keating, Landrum, Rose (2017). Classifying lung cancer severity with ensemble machine learning in health care claims data, Proceedings of Machine Learning Research. [PDF]
Kessler 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]
Guidelines & Standards for AI/ML
Collins et al. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods, BMJ. [Link]
Muralidharan, Burgart, Danehsjou, Rose (2023). Recommendations for the use of pediatric data in artificial intelligence and machine learning: ACCEPT-AI, npj Digital Medicine. [Link]
McElfresh et al. (2023). A call for better validation of opioid overdose risk algorithms, JAMIA. [Link]
Sounderajah et al. (2022). Ethics methods are required as part of reporting guidelines for artificial intelligence in healthcare, Nature Machine Intelligence. [Link]
Sounderajah et al. (2021). A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI, Nature Medicine. [Link]
Rose (2018). Machine learning for prediction in electronic health data, JAMA Network Open. [Link][Press coverage in Politico]
Program & Policy Evaluation
McDowell, Raifman, Progovac, 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]
Carroll, Chernew, Fendrick, Thompson, 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]
Mateen, McKenzie, Rose (2018). Medical schools in fragile states: Implications for delivery of care, Health Services Research. [Link][with Discussion]
Spertus, Normand, Wolf, Cioffi, Lovett, Rose (2016). Assessing hospital performance after percutaneous coronary intervention using big data, Circulation: Cardiovascular Quality and Outcomes. [Link]
Song, Rose, Safran, Landon, Day, 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
Majumder, Cusick, Rose (2023). Measuring concordance of data sources used for infectious disease research in the USA: A retrospective data analysis, BMJ Open. [Link][medRxiv Version]
Majumder, Rose (2021). A generalizable data assembly algorithm for infectious disease outbreaks, JAMIA Open. [Link][medRxiv Version][Code]
Carrell et al. (2017). Challenges in adapting existing clinical natural language processing systems to multiple diverse healthcare settings, JAMIA. [Link]
Telemedicine
Uscher-Pines, Riedel, Mehrotra, Rose, Busch, Huskamp (2023). Many clinicians implement digital equity strategies to treat opioid use disorder, Health Affairs. [Link]
Patel, Rose, Barnett, Huskamp, Uscher-Pines, Mehrotra (2021). Community factors associated with telemedicine use during the COVID-19 pandemic, JAMA Network Open. [Link]
McDowell, Huskamp, Busch, Mehrotra, Rose (2021). Patterns of mental health care before initiation of telemental health services, Medical Care. [Link]
Wilcock, Rose, Busch, Huskamp, Uscher-Pines, Landon, Mehrotra (2019). Association between broadband internet availability and telemedicine use, JAMA Internal Medicine. [Link][Press Coverage in US News]
Huskamp, 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]
Mehrotra et al. (2017). Rapid growth in mental health telemedicine use among rural Medicare beneficiaries, wide variation across states, Health Affairs. [Link]