For complete list, see CV.         


Risk Adjustment

A. Zink, S. Rose (2019). Fair regression for health care spending. [arXiv Version] [Code]

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, T. McGuire (2019). Limitations of p-values and R-squared for stepwise regression building: A fairness demonstration in health policy risk adjustment, The American Statistician. [PDF]

S. Bergquist, T. McGuire, T. Layton, S. Rose (2018). Sample selection for Medicare risk adjustment due to systematically missing data, Health Services Research. [PDF]

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]

A. Shrestha, S. Bergquist*, E. Montz, S. Rose (2018). Mental health risk adjustment with clinical categories and machine learning, Health Services Research. [PDF]

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 BlogHealthExec, CHSR, The Source]

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

S. Rose, J. Shi, T. McGuire, S.L. Normand (2015). Matching and imputation methods for risk adjustment in the Health Insurance Marketplaces, Statistics in Biosciences. [PDF]


Evaluation and Causality

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

S. Adhikari, S. Rose, S.L. Normand (2019). 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]

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]

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

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 TimesUS NewsThe Boston Globe]

S. Rose, M.J. van der Laan (2014). A double robust approach to causal effects in case-control studies, American Journal of Epidemiology. [PDF][with Discussion and Invited Reply]

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]


Health Policy Prediction

M. Majumder, S. Rose (2018). Vaccine deployment and Ebola transmission dynamics estimation in Eastern DR Congo. [SSRN Version]

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]

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

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]

R. Kessler, S. Rose, et al. (2014). How well can post-trauamtic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys, World Psychiatry. [PDF]

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]