These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

155 related articles for article (PubMed ID: 32843815)

  • 1. Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods.
    Ju C; Combs M; Lendle SD; Franklin JM; Wyss R; Schneeweiss S; van der Laan MJ
    J Appl Stat; 2019; 46(12):2216-2236. PubMed ID: 32843815
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Combining Super Learner with high-dimensional propensity score to improve confounding adjustment: A real-world application in chronic lymphocytic leukemia.
    Dhopeshwarkar N; Yang W; Hennessy S; Rhodes JM; Cuker A; Leonard CE
    Pharmacoepidemiol Drug Saf; 2024 Jan; 33(1):e5678. PubMed ID: 37609668
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Using Super Learner Prediction Modeling to Improve High-dimensional Propensity Score Estimation.
    Wyss R; Schneeweiss S; van der Laan M; Lendle SD; Ju C; Franklin JM
    Epidemiology; 2018 Jan; 29(1):96-106. PubMed ID: 28991001
    [TBL] [Abstract][Full Text] [Related]  

  • 4. High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions.
    Neugebauer R; Schmittdiel JA; Zhu Z; Rassen JA; Seeger JD; Schneeweiss S
    Stat Med; 2015 Feb; 34(5):753-81. PubMed ID: 25488047
    [TBL] [Abstract][Full Text] [Related]  

  • 5. The Balance Super Learner: A robust adaptation of the Super Learner to improve estimation of the average treatment effect in the treated based on propensity score matching.
    Pirracchio R; Carone M
    Stat Methods Med Res; 2018 Aug; 27(8):2504-2518. PubMed ID: 28339317
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Improving propensity score estimators' robustness to model misspecification using super learner.
    Pirracchio R; Petersen ML; van der Laan M
    Am J Epidemiol; 2015 Jan; 181(2):108-19. PubMed ID: 25515168
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Parametric and nonparametric propensity score estimation in multilevel observational studies.
    Salditt M; Nestler S
    Stat Med; 2023 Oct; 42(23):4147-4176. PubMed ID: 37532119
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Practical considerations for specifying a super learner.
    Phillips RV; van der Laan MJ; Lee H; Gruber S
    Int J Epidemiol; 2023 Aug; 52(4):1276-1285. PubMed ID: 36905602
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Super Learner for Survival Data Prediction.
    Golmakani MK; Polley EC
    Int J Biostat; 2020 Feb; ():. PubMed ID: 32097120
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A two-stage super learner for healthcare expenditures.
    Wu Z; Berkowitz SA; Heagerty PJ; Benkeser D
    Health Serv Outcomes Res Methodol; 2022 Dec; 22(4):435-453. PubMed ID: 36437854
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Scalable collaborative targeted learning for high-dimensional data.
    Ju C; Gruber S; Lendle SD; Chambaz A; Franklin JM; Wyss R; Schneeweiss S; van der Laan MJ
    Stat Methods Med Res; 2019 Feb; 28(2):532-554. PubMed ID: 28936917
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Using electronic health records to identify candidates for human immunodeficiency virus pre-exposure prophylaxis: An application of super learning to risk prediction when the outcome is rare.
    Gruber S; Krakower D; Menchaca JT; Hsu K; Hawrusik R; Maro JC; Cocoros NM; Kruskal BA; Wilson IB; Mayer KH; Klompas M
    Stat Med; 2020 Oct; 39(23):3059-3073. PubMed ID: 32578905
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A hybrid super ensemble learning model for the early-stage prediction of diabetes risk.
    Doğru A; Buyrukoğlu S; Arı M
    Med Biol Eng Comput; 2023 Mar; 61(3):785-797. PubMed ID: 36602674
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets.
    Gruber S; Logan RW; Jarrín I; Monge S; Hernán MA
    Stat Med; 2015 Jan; 34(1):106-17. PubMed ID: 25316152
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm.
    Cherifa M; Blet A; Chambaz A; Gayat E; Resche-Rigon M; Pirracchio R
    Anesth Analg; 2020 May; 130(5):1157-1166. PubMed ID: 32287123
    [TBL] [Abstract][Full Text] [Related]  

  • 16. High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting.
    Rassen JA; Blin P; Kloss S; Neugebauer RS; Platt RW; Pottegård A; Schneeweiss S; Toh S
    Pharmacoepidemiol Drug Saf; 2023 Feb; 32(2):93-106. PubMed ID: 36349471
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study.
    Wong J; Manderson T; Abrahamowicz M; Buckeridge DL; Tamblyn R
    Epidemiology; 2019 Jul; 30(4):521-531. PubMed ID: 30985529
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Improved Prediction of Body Mass Index in Real-World Administrative Healthcare Claims Databases.
    Lan G; Wu B; Sharma K; Gadhia K; Ashton V
    Adv Ther; 2022 Aug; 39(8):3835-3844. PubMed ID: 35680715
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Variable Selection for Confounding Adjustment in High-dimensional Covariate Spaces When Analyzing Healthcare Databases.
    Schneeweiss S; Eddings W; Glynn RJ; Patorno E; Rassen J; Franklin JM
    Epidemiology; 2017 Mar; 28(2):237-248. PubMed ID: 27779497
    [TBL] [Abstract][Full Text] [Related]  

  • 20.
    ; ; . PubMed ID:
    [No Abstract]   [Full Text] [Related]  

    [Next]    [New Search]
    of 8.