Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection
暂无分享,去创建一个
Mi-Ok Kim | Yizeng He | Soyoung Kim | Wael Saber | Kwang Woo Ahn | Mi-Ok Kim | W. Saber | K. Ahn | Soyoung Kim | Yizeng He
[1] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[2] Mei‐jie Zhang,et al. A Proportional Hazards Regression Model for the Subdistribution with Covariates‐adjusted Censoring Weight for Competing Risks Data , 2016, Scandinavian journal of statistics, theory and applications.
[3] Jian Huang,et al. A Selective Review of Group Selection in High-Dimensional Models. , 2012, Statistical science : a review journal of the Institute of Mathematical Statistics.
[4] J. Kanda,et al. Peripheral Blood versus Bone Marrow from Unrelated Donors: Bone Marrow Allografts Have Improved Long-Term Overall and Graft-versus-Host Disease-Free, Relapse-Free Survival. , 2019, Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation.
[5] I. Ha,et al. Comparison of the marginal hazard model and the sub-distribution hazard model for competing risks under an assumed copula , 2019, Statistical methods in medical research.
[6] Donglin Zeng,et al. On sparse representation for optimal individualized treatment selection with penalized outcome weighted learning , 2015, Stat.
[7] Marie Davidian,et al. Optimal two‐stage dynamic treatment regimes from a classification perspective with censored survival data , 2018, Biometrics.
[8] Peisong Han,et al. Multiply Robust Estimation in Regression Analysis With Missing Data , 2014 .
[9] Wenbin Lu,et al. On restricted optimal treatment regime estimation for competing risks data. , 2019, Biostatistics.
[10] Lu Wang,et al. Estimation with missing data: beyond double robustness , 2013 .
[11] Mei-Jie Zhang,et al. Marginal Models for Clustered Time‐to‐Event Data with Competing Risks Using Pseudovalues , 2011, Biometrics.
[12] Giota Touloumi,et al. Practical methods for competing risks data: A review , 2012, Statistical methods in medical research.
[13] Medhat Askar,et al. Nonpermissive HLA-DPB1 mismatch increases mortality after myeloablative unrelated allogeneic hematopoietic cell transplantation. , 2014, Blood.
[14] Wenbin Lu,et al. Variable selection for optimal treatment decision , 2013, Statistical methods in medical research.
[15] Eric B. Laber,et al. Doubly Robust Learning for Estimating Individualized Treatment with Censored Data. , 2015, Biometrika.
[16] A. Wahed,et al. Estimating the cumulative incidence function of dynamic treatment regimes , 2018 .
[17] Wenbin Lu,et al. DOUBLY ROBUST ESTIMATION OF OPTIMAL TREATMENT REGIMES FOR SURVIVAL DATA-WITH APPLICATION TO AN HIV/AIDS STUDY. , 2017, The annals of applied statistics.
[18] P. Westervelt,et al. Peripheral-blood stem cells versus bone marrow from unrelated donors. , 2012, The New England journal of medicine.
[19] Kwang Woo Ahn,et al. Group and within-group variable selection for competing risks data , 2018, Lifetime data analysis.
[20] J. Klein,et al. Generalised linear models for correlated pseudo‐observations, with applications to multi‐state models , 2003 .
[21] Robert Gray,et al. A Proportional Hazards Model for the Subdistribution of a Competing Risk , 1999 .
[22] Chengchun Shi,et al. HIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES. , 2018, Annals of statistics.
[23] Wenbin Lu,et al. Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective , 2017, Lifetime data analysis.
[24] D.,et al. Regression Models and Life-Tables , 2022 .
[25] R. Geskus,et al. A comparison of model selection methods for prediction in the presence of multiply imputed data , 2018, Biometrical journal. Biometrische Zeitschrift.
[26] Sijian Wang,et al. Doubly regularized Cox regression for high-dimensional survival data with group structures , 2013 .
[27] W. Bensinger. Allogeneic transplantation: peripheral blood vs. bone marrow , 2012, Current opinion in oncology.
[28] Donald B. Rubin,et al. Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .
[29] Min Zhang,et al. Estimating optimal treatment regimes from a classification perspective , 2012, Stat.
[30] Cun-Hui Zhang,et al. A group bridge approach for variable selection , 2009, Biometrika.