Daniel O. Scharfstein
Affiliations: | Johns Hopkins University, Baltimore, MD |
Area:
Biostatistics Biology, Statistics, EpidemiologyGoogle:
"Daniel Scharfstein"Children
Sign in to add trainee| Brian L. Egleston | grad student | 2006 | Johns Hopkins |
| Weiwei Wang | grad student | 2008 | Johns Hopkins |
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Publications
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| Ogburn EL, Bierer BE, Brookmeyer R, et al. (2020) Aggregating data from COVID-19 trials. Science (New York, N.Y.). 368: 1198-1199 |
| Xu Y, Scharfstein D, Müller P, et al. (2020) A Bayesian nonparametric approach for evaluating the causal effect of treatment in randomized trials with semi-competing risks. Biostatistics (Oxford, England) |
| Wang C, Colantuoni E, Leroux A, et al. (2020) idem: An R Package for Inferences in Clinical Trials with Death and Missingness Journal of Statistical Software. 93 |
| Gitajn IL, Reider L, Scharfstein DO, et al. (2019) Variability in discharge disposition across U.S. trauma centers following treatment for high-energy lower extremity injuries. Journal of Orthopaedic Trauma |
| Magnusson BP, Schmidli H, Rouyrre N, et al. (2019) Bayesian inference for a principal stratum estimand to assess the treatment effect in a subgroup characterized by postrandomization event occurrence. Statistics in Medicine |
| Castillo RC, Huang Y, Scharfstein D, et al. (2018) Association Between 6-Week Postdischarge Risk Classification and 12-Month Outcomes After Orthopedic Trauma. Jama Surgery. e184824 |
| Scharfstein DO, McDermott A. (2018) Global sensitivity analysis of clinical trials with missing patient-reported outcomes. Statistical Methods in Medical Research. 962280218759565 |
| Hernán MA, Scharfstein D. (2018) Cautions as Regulators Move to End Exclusive Reliance on Intention to Treat. Annals of Internal Medicine. 168: 515-516 |
| Colantuoni E, Scharfstein DO, Wang C, et al. (2018) Statistical methods to compare functional outcomes in randomized controlled trials with high mortality. Bmj (Clinical Research Ed.). 360: j5748 |
| Scharfstein D, McDermott A, Díaz I, et al. (2017) Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach. Biometrics |