BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

162 related articles for article (PubMed ID: 34490942)

  • 1. Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data.
    Tang M; Wang L; Gorin MA; Taylor JMG
    Stat Med; 2021 Nov; 40(27):6164-6177. PubMed ID: 34490942
    [TBL] [Abstract][Full Text] [Related]  

  • 2. TREE-BASED REINFORCEMENT LEARNING FOR ESTIMATING OPTIMAL DYNAMIC TREATMENT REGIMES.
    Tao Y; Wang L; Almirall D
    Ann Appl Stat; 2018 Sep; 12(3):1914-1938. PubMed ID: 30984321
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Multiobjective tree-based reinforcement learning for estimating tolerant dynamic treatment regimes.
    Song Y; Wang L
    Biometrics; 2024 Jan; 80(1):. PubMed ID: 38364801
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Restricted sub-tree learning to estimate an optimal dynamic treatment regime using observational data.
    Speth K; Wang L
    Stat Med; 2021 Nov; 40(26):5796-5812. PubMed ID: 34340264
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Imputation-based Q-learning for optimizing dynamic treatment regimes with right-censored survival outcome.
    Lyu L; Cheng Y; Wahed AS
    Biometrics; 2023 Dec; 79(4):3676-3689. PubMed ID: 37129942
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Penalized Spline-Involved Tree-based (PenSIT) Learning for estimating an optimal dynamic treatment regime using observational data.
    Speth KA; Elliott MR; Marquez JL; Wang L
    Stat Methods Med Res; 2022 Dec; 31(12):2338-2351. PubMed ID: 36189475
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Estimating tree-based dynamic treatment regimes using observational data with restricted treatment sequences.
    Zhou N; Wang L; Almirall D
    Biometrics; 2023 Sep; 79(3):2260-2271. PubMed ID: 36063542
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Optimal dynamic treatment regime estimation using information extraction from unstructured clinical text.
    Zhou N; Brook RD; Dinov ID; Wang L
    Biom J; 2022 Apr; 64(4):805-817. PubMed ID: 35112726
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network.
    Liu N; Liu Y; Logan B; Xu Z; Tang J; Wang Y
    Sci Rep; 2019 Feb; 9(1):1495. PubMed ID: 30728403
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content.
    Orellana L; Rotnitzky A; Robins JM
    Int J Biostat; 2010; 6(2):Article 8. PubMed ID: 21969994
    [TBL] [Abstract][Full Text] [Related]  

  • 14. New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.
    Zhao YQ; Zeng D; Laber EB; Kosorok MR
    J Am Stat Assoc; 2015; 110(510):583-598. PubMed ID: 26236062
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Bayesian inference for optimal dynamic treatment regimes in practice.
    Rodriguez Duque D; Moodie EEM; Stephens DA
    Int J Biostat; 2023 Nov; 19(2):309-331. PubMed ID: 37192544
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Optimization of multi-stage dynamic treatment regimes utilizing accumulated data.
    Huang X; Choi S; Wang L; Thall PF
    Stat Med; 2015 Nov; 34(26):3424-43. PubMed ID: 26095711
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Q-learning for estimating optimal dynamic treatment rules from observational data.
    Moodie EE; Chakraborty B; Kramer MS
    Can J Stat; 2012 Dec; 40(4):629-645. PubMed ID: 23355757
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Identifying a set that contains the best dynamic treatment regimes.
    Ertefaie A; Wu T; Lynch KG; Nahum-Shani I
    Biostatistics; 2016 Jan; 17(1):135-48. PubMed ID: 26243172
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data.
    Liu Y; Logan B; Liu N; Xu Z; Tang J; Wang Y
    Healthc Inform; 2017 Aug; 2017():380-385. PubMed ID: 29556119
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Cost-Effectiveness Analysis of Stockholm 3 Testing Compared to PSA as the Primary Blood Test in the Prostate Cancer Diagnostic Pathway: A Decision Tree Approach.
    Risør BW; Tayyari Dehbarez N; Fredsøe J; Sørensen KD; Pedersen BG
    Appl Health Econ Health Policy; 2022 Nov; 20(6):867-880. PubMed ID: 35934771
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.