| Year |
Citation |
Score |
| 2020 |
Rose S. Intersections of machine learning and epidemiological methods for health services research. International Journal of Epidemiology. PMID 32236476 DOI: 10.1093/Ije/Dyaa035 |
0.318 |
|
| 2019 |
Zink A, Rose S. Fair regression for health care spending. Biometrics. PMID 31860120 DOI: 10.1111/Biom.13206 |
0.336 |
|
| 2019 |
McDowell A, Progovac AM, Cook BL, Rose S. Estimating the Health Status of Privately Insured Gender Minority Children and Adults. Lgbt Health. PMID 31314674 DOI: 10.1089/Lgbt.2018.0238 |
0.304 |
|
| 2019 |
Blakely T, Lynch J, Simons K, Bentley R, Rose S. Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference. International Journal of Epidemiology. PMID 31298274 DOI: 10.1093/Ije/Dyz132 |
0.34 |
|
| 2019 |
Rose S, McGuire TG. Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment. The American Statistician. 73: 152-156. PMID 31263291 DOI: 10.1080/00031305.2018.1518269 |
0.309 |
|
| 2018 |
Rose S. Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending. Health Services Research. PMID 29527659 DOI: 10.1111/1475-6773.12848 |
0.316 |
|
| 2018 |
Bergquist SL, Layton TJ, McGuire TG, Rose S. Intervening on the Data to Improve the Performance of Health Plan Payment Methods National Bureau of Economic Research. DOI: 10.3386/W24491 |
0.346 |
|
| 2017 |
Rose S, Shi J, McGuire TG, Normand ST. Matching and Imputation Methods for Risk Adjustment in the Health Insurance Marketplaces. Statistics in Biosciences. 9: 525-542. PMID 29484032 DOI: 10.1007/S12561-015-9135-7 |
0.358 |
|
| 2017 |
Shrestha A, Bergquist S, Montz E, Rose S. Mental Health Risk Adjustment with Clinical Categories and Machine Learning. Health Services Research. PMID 29244202 DOI: 10.1111/1475-6773.12818 |
0.321 |
|
| 2017 |
Rosellini AJ, Dussaillant F, Zubizarreta JR, Kessler RC, Rose S. Predicting posttraumatic stress disorder following a natural disaster. Journal of Psychiatric Research. 96: 15-22. PMID 28950110 DOI: 10.1016/J.Jpsychires.2017.09.010 |
0.318 |
|
| 2017 |
Rose S, Bergquist SL, Layton TJ. Computational health economics for identification of unprofitable health care enrollees. Biostatistics (Oxford, England). PMID 28369273 DOI: 10.1093/Biostatistics/Kxx012 |
0.324 |
|
| 2016 |
Schuler MS, Rose S. Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies. American Journal of Epidemiology. PMID 27941068 DOI: 10.1093/Aje/Kww165 |
0.377 |
|
| 2016 |
Montz E, Layton T, Busch AB, Ellis RP, Rose S, McGuire TG. Risk-Adjustment Simulation: Plans May Have Incentives To Distort Mental Health And Substance Use Coverage. Health Affairs (Project Hope). 35: 1022-8. PMID 27269018 DOI: 10.1377/Hlthaff.2015.1668 |
0.303 |
|
| 2016 |
Rose S, Zaslavsky AM, McWilliams JM. Variation In Accountable Care Organization Spending And Sensitivity To Risk Adjustment: Implications For Benchmarking. Health Affairs (Project Hope). 35: 440-8. PMID 26953298 DOI: 10.1377/Hlthaff.2015.1026 |
0.315 |
|
| 2016 |
Rose S. A Machine Learning Framework for Plan Payment Risk Adjustment. Health Services Research. PMID 26891974 DOI: 10.1111/1475-6773.12464 |
0.362 |
|
| 2016 |
Mirelman AJ, Rose S, Khan JA, Ahmed S, Peters DH, Niessen LW, Trujillo AJ. The relationship between non-communicable disease occurrence and poverty-evidence from demographic surveillance in Matlab, Bangladesh. Health Policy and Planning. PMID 26843515 DOI: 10.1093/Heapol/Czv134 |
0.33 |
|
| 2014 |
Kessler RC, Rose S, Koenen KC, Karam EG, Stang PE, Stein DJ, Heeringa SG, Hill ED, Liberzon I, McLaughlin KA, McLean SA, Pennell BE, Petukhova M, Rosellini AJ, Ruscio AM, et al. How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys. World Psychiatry : Official Journal of the World Psychiatric Association (Wpa). 13: 265-74. PMID 25273300 DOI: 10.1002/Wps.20150 |
0.306 |
|
| 2014 |
Wang H, Zhang Z, Rose S, van der Laan M. A novel targeted learning method for quantitative trait loci mapping. Genetics. 198: 1369-76. PMID 25258376 DOI: 10.1534/Genetics.114.168955 |
0.528 |
|
| 2014 |
Rose S, van der Laan M. Rose and van der Laan respond to "Some advantages of the relative excess risk due to interaction". American Journal of Epidemiology. 179: 672-3. PMID 24488516 DOI: 10.1093/Aje/Kwt317 |
0.545 |
|
| 2014 |
Rose S, van der Laan M. A double robust approach to causal effects in case-control studies. American Journal of Epidemiology. 179: 663-9. PMID 24488515 DOI: 10.1093/Aje/Kwt318 |
0.529 |
|
| 2013 |
Rose S. Mortality risk score prediction in an elderly population using machine learning. American Journal of Epidemiology. 177: 443-52. PMID 23364879 DOI: 10.1093/Aje/Kws241 |
0.312 |
|
| 2011 |
Wang H, Rose S, van der Laan MJ. Finding Quantitative Trait Loci Genes with Collaborative Targeted Maximum Likelihood Learning. Statistics & Probability Letters. 81: 792-796. PMID 21572586 DOI: 10.1016/J.Spl.2010.11.001 |
0.512 |
|
| 2011 |
Rose S, van der Laan MJ. A targeted maximum likelihood estimator for two-stage designs. The International Journal of Biostatistics. 7: 17. PMID 21556285 DOI: 10.2202/1557-4679.1217 |
0.506 |
|
| 2009 |
Rose S, Laan MJ. Why match? Investigating matched case-control study designs with causal effect estimation. The International Journal of Biostatistics. 5: Article 1. PMID 20231866 DOI: 10.2202/1557-4679.1127 |
0.334 |
|
| 2008 |
Rose S, van der Laan MJ. Simple optimal weighting of cases and controls in case-control studies. The International Journal of Biostatistics. 4: Article 19. PMID 20231910 DOI: 10.2202/1557-4679.1115 |
0.525 |
|
| Show low-probability matches. |