| Targeted Learning: Causal Inference for Observational and Experimental Data MJ van der Laan, S Rose Springer [BOOK], 2011 | 1648 | 2011 |
| Ethical Machine Learning in Healthcare IY Chen, E Pierson, S Rose, S Joshi, K Ferryman, M Ghassemi Annual Review of Biomedical Data Science 4, 123-144, 2021 | 508 | 2021 |
| Implementation of G-computation on a simulated data set: demonstration of a causal inference technique JM Snowden, S Rose, KM Mortimer American Journal of Epidemiology 173 (7), 731-738, 2011 | 477 | 2011 |
| Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) RC Kessler, CH Warner, C Ivany, MV Petukhova, S Rose, EJ Bromet, ... JAMA Psychiatry 72 (1), 49-57, 2015 | 463 | 2015 |
| Targeted maximum likelihood estimation for causal inference in observational studies MS Schuler, S Rose American Journal of Epidemiology 185 (1), 65-73, 2017 | 382 | 2017 |
| TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods GS Collins, KGM Moons, P Dhiman, RD Riley, AL Beam, B Van Calster, ... BMJ 385, 2024 | 379 | 2024 |
| How well can post‐traumatic stress disorder be predicted from pre‐trauma risk factors? An exploratory study in the WHO World Mental Health Surveys RC Kessler, S Rose, KC Koenen, EG Karam, PE Stang, DJ Stein, ... World Psychiatry 13 (3), 265-274, 2014 | 360 | 2014 |
| Why match? Investigating matched case-control study designs with causal effect estimation S Rose, MJ Van der Laan International Journal of Biostatistics 5 (1), 2009 | 342 | 2009 |
| Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies MJ van der Laan, S Rose Springer [BOOK], 2018 | 333* | 2018 |
| A review of generalizability and transportability I Degtiar, S Rose Annual Review of Statistics and Its Application 10 (1), 501-524, 2023 | 289 | 2023 |
| Changes in health care spending and quality 4 years into global payment Z Song, S Rose, DG Safran, BE Landon, MP Day, ME Chernew New England Journal of Medicine 371 (18), 1704-1714, 2014 | 279 | 2014 |
| Mortality risk score prediction in an elderly population using machine learning S Rose American Journal of Epidemiology 177 (5), 443-452, 2013 | 212 | 2013 |
| Rapid growth in mental health telemedicine use among rural Medicare beneficiaries, wide variation across states A Mehrotra, HA Huskamp, J Souza, L Uscher-Pines, S Rose, BE Landon, ... Health Affairs 36 (5), 909-917, 2017 | 204 | 2017 |
| How is telemedicine being used in opioid and other substance use disorder treatment? HA Huskamp, AB Busch, J Souza, L Uscher-Pines, S Rose, A Wilcock, ... Health Affairs 37 (12), 1940-1947, 2018 | 173 | 2018 |
| Super learning EC Polley, S Rose, MJ Van der Laan Targeted Learning, 2011 | 164 | 2011 |
| A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI V Sounderajah, H Ashrafian, S Rose, NH Shah, M Ghassemi, R Golub, ... Nature Medicine 27 (10), 1663-1665, 2021 | 135 | 2021 |
| Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings DS Carrell, RE Schoen, DA Leffler, M Morris, S Rose, A Baer, SD Crockett, ... Journal of the American Medical Informatics Association 24 (5), 986-991, 2017 | 128 | 2017 |
| A machine learning framework for plan payment risk adjustment S Rose Health Services Research 51 (6), 2358-2374, 2016 | 107 | 2016 |
| Machine learning for prediction in electronic health data S Rose JAMA Network Open 1 (4), e181404-e181404, 2018 | 104 | 2018 |
| Reflection on modern methods: when worlds collide—prediction, machine learning and causal inference T Blakely, J Lynch, K Simons, R Bentley, S Rose International Journal of Epidemiology 49 (6), 2058-2064, 2020 | 103 | 2020 |