166 related articles for article (PubMed ID: 30931437)
41. A parsimonious personalized dose-finding model via dimension reduction.
Zhou W; Zhu R; Zeng D
Biometrika; 2021 Sep; 108(3):643-659. PubMed ID: 34658383
[TBL] [Abstract][Full Text] [Related]
42. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.
Woldaregay AZ; Årsand E; Walderhaug S; Albers D; Mamykina L; Botsis T; Hartvigsen G
Artif Intell Med; 2019 Jul; 98():109-134. PubMed ID: 31383477
[TBL] [Abstract][Full Text] [Related]
43. Design considerations and analytical framework for reliably identifying a beneficial individualized treatment rule.
Cain CH; Murray TA; Rudser KD; Rothman AJ; Melzer AC; Joseph AM; Vock DM
Contemp Clin Trials; 2022 Dec; 123():106951. PubMed ID: 36241146
[TBL] [Abstract][Full Text] [Related]
44. Estimating individualized treatment rules for multicategory type 2 diabetes treatments using electronic health records.
Lou J; Wang Y; Li L; Zeng D
Stat Interface; 2023; 16(4):505-515. PubMed ID: 38344146
[TBL] [Abstract][Full Text] [Related]
45. Efficient augmentation and relaxation learning for individualized treatment rules using observational data.
Zhao YQ; Laber EB; Ning Y; Saha S; Sands BE
J Mach Learn Res; 2019; 20():. PubMed ID: 31440118
[TBL] [Abstract][Full Text] [Related]
46. Entropy Learning for Dynamic Treatment Regimes.
Jiang B; Song R; Li J; Zeng D
Stat Sin; 2019; 29(4):1633-1655. PubMed ID: 31534307
[TBL] [Abstract][Full Text] [Related]
47. Contrast weighted learning for robust optimal treatment rule estimation.
Guo X; Ni A
Stat Med; 2022 Nov; 41(27):5379-5394. PubMed ID: 36104931
[TBL] [Abstract][Full Text] [Related]
48. Adaptive contrast weighted learning for multi-stage multi-treatment decision-making.
Tao Y; Wang L
Biometrics; 2017 Mar; 73(1):145-155. PubMed ID: 27213913
[TBL] [Abstract][Full Text] [Related]
49. Personalized Dose Finding Using Outcome Weighted Learning.
Chen G; Zeng D; Kosorok MR
J Am Stat Assoc; 2016; 111(516):1509-1521. PubMed ID: 28255189
[TBL] [Abstract][Full Text] [Related]
50. Greedy outcome weighted tree learning of optimal personalized treatment rules.
Zhu R; Zhao YQ; Chen G; Ma S; Zhao H
Biometrics; 2017 Jun; 73(2):391-400. PubMed ID: 27704531
[TBL] [Abstract][Full Text] [Related]
51. Use of personalized Dynamic Treatment Regimes (DTRs) and Sequential Multiple Assignment Randomized Trials (SMARTs) in mental health studies.
Liu Y; Zeng D; Wang Y
Shanghai Arch Psychiatry; 2014 Dec; 26(6):376-83. PubMed ID: 25642116
[TBL] [Abstract][Full Text] [Related]
52. High-Dimensional Precision Medicine From Patient-Derived Xenografts.
Rashid NU; Luckett DJ; Chen J; Lawson MT; Wang L; Zhang Y; Laber EB; Liu Y; Yeh JJ; Zeng D; Kosorok MR
J Am Stat Assoc; 2021; 116(535):1140-1154. PubMed ID: 34548714
[TBL] [Abstract][Full Text] [Related]
53. Learning Optimal Individualized Treatment Rules from Electronic Health Record Data.
Wang Y; Wu P; Liu Y; Weng C; Zeng D
IEEE Int Conf Healthc Inform; 2016 Oct; 2016():65-71. PubMed ID: 28503676
[TBL] [Abstract][Full Text] [Related]
54. Tree-based methods for individualized treatment regimes.
Laber EB; Zhao YQ
Biometrika; 2015; 102(3):501-514. PubMed ID: 26893526
[TBL] [Abstract][Full Text] [Related]
55. Utility-based Weighted Multicategory Robust Support Vector Machines.
Liu Y; Wu Y; He Q
Stat Interface; 2010 Oct; 3(4):465-476. PubMed ID: 23894688
[TBL] [Abstract][Full Text] [Related]
56. Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data.
Hager R; Tsiatis AA; Davidian M
Biometrics; 2018 Dec; 74(4):1180-1192. PubMed ID: 29775203
[TBL] [Abstract][Full Text] [Related]
57. Treatment decisions based on scalar and functional baseline covariates.
Ciarleglio A; Petkova E; Ogden RT; Tarpey T
Biometrics; 2015 Dec; 71(4):884-94. PubMed ID: 26111145
[TBL] [Abstract][Full Text] [Related]
58. Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes.
Chen Y; Zeng D; Wang Y
J Am Stat Assoc; 2021; 116(533):269-282. PubMed ID: 34776561
[TBL] [Abstract][Full Text] [Related]
59. Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes.
Wu D; Goldfeld KS; Petkova E; Park HG
medRxiv; 2024 Jan; ():. PubMed ID: 38014277
[TBL] [Abstract][Full Text] [Related]
60. C-learning: A new classification framework to estimate optimal dynamic treatment regimes.
Zhang B; Zhang M
Biometrics; 2018 Sep; 74(3):891-899. PubMed ID: 29228509
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
