BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

433 related articles for article (PubMed ID: 24589914)

  • 1. Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study.
    Shah AD; Bartlett JW; Carpenter J; Nicholas O; Hemingway H
    Am J Epidemiol; 2014 Mar; 179(6):764-74. PubMed ID: 24589914
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A fair comparison of tree-based and parametric methods in multiple imputation by chained equations.
    Slade E; Naylor MG
    Stat Med; 2020 Apr; 39(8):1156-1166. PubMed ID: 31997388
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Imputing missing covariates in time-to-event analysis within distributed research networks: A simulation study.
    Li D; Wong J; Li X; Toh S; Wang R
    Pharmacoepidemiol Drug Saf; 2023 Mar; 32(3):330-340. PubMed ID: 36380400
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Performance of Multiple Imputation Using Modern Machine Learning Methods in Electronic Health Records Data.
    Getz K; Hubbard RA; Linn KA
    Epidemiology; 2023 Mar; 34(2):206-215. PubMed ID: 36722803
    [TBL] [Abstract][Full Text] [Related]  

  • 5. SuperMICE: An Ensemble Machine Learning Approach to Multiple Imputation by Chained Equations.
    Laqueur HS; Shev AB; Kagawa RMC
    Am J Epidemiol; 2022 Feb; 191(3):516-525. PubMed ID: 34788362
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Imputation strategies when a continuous outcome is to be dichotomized for responder analysis: a simulation study.
    Floden L; Bell ML
    BMC Med Res Methodol; 2019 Jul; 19(1):161. PubMed ID: 31345166
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.
    Marshall A; Altman DG; Royston P; Holder RL
    BMC Med Res Methodol; 2010 Jan; 10():7. PubMed ID: 20085642
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Multiple imputation for handling missing outcome data when estimating the relative risk.
    Sullivan TR; Lee KJ; Ryan P; Salter AB
    BMC Med Res Methodol; 2017 Sep; 17(1):134. PubMed ID: 28877666
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Logistic regression vs. predictive mean matching for imputing binary covariates.
    Austin PC; van Buuren S
    Stat Methods Med Res; 2023 Nov; 32(11):2172-2183. PubMed ID: 37750213
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction.
    Hong S; Lynn HS
    BMC Med Res Methodol; 2020 Jul; 20(1):199. PubMed ID: 32711455
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Imputation of missing values of tumour stage in population-based cancer registration.
    Eisemann N; Waldmann A; Katalinic A
    BMC Med Res Methodol; 2011 Sep; 11():129. PubMed ID: 21929796
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multiple imputation with missing data indicators.
    Beesley LJ; Bondarenko I; Elliot MR; Kurian AW; Katz SJ; Taylor JM
    Stat Methods Med Res; 2021 Dec; 30(12):2685-2700. PubMed ID: 34643465
    [TBL] [Abstract][Full Text] [Related]  

  • 13. MISL: Multiple imputation by super learning.
    Carpenito T; Manjourides J
    Stat Methods Med Res; 2022 Oct; 31(10):1904-1915. PubMed ID: 35658622
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Generative adversarial networks for imputing missing data for big data clinical research.
    Dong W; Fong DYT; Yoon JS; Wan EYF; Bedford LE; Tang EHM; Lam CLK
    BMC Med Res Methodol; 2021 Apr; 21(1):78. PubMed ID: 33879090
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.
    Kokla M; Virtanen J; Kolehmainen M; Paananen J; Hanhineva K
    BMC Bioinformatics; 2019 Oct; 20(1):492. PubMed ID: 31601178
    [TBL] [Abstract][Full Text] [Related]  

  • 16. [Multiple imputation of missing at random data: General points and presentation of a Monte-Carlo method].
    Cottrell G; Cot M; Mary JY
    Rev Epidemiol Sante Publique; 2009 Oct; 57(5):361-72. PubMed ID: 19674855
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Imputing missing time-dependent covariate values for the discrete time Cox model.
    Murad H; Dankner R; Berlin A; Olmer L; Freedman LS
    Stat Methods Med Res; 2020 Aug; 29(8):2074-2086. PubMed ID: 31680633
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study.
    Marshall A; Altman DG; Holder RL
    BMC Med Res Methodol; 2010 Dec; 10():112. PubMed ID: 21194416
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry.
    Steif J; Brant R; Sreepada RS; West N; Murthy S; Görges M
    Pediatr Crit Care Med; 2022 Jan; 23(1):e29-e44. PubMed ID: 34560774
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Comparison of Imputation Strategies for Incomplete Longitudinal Data in Life-Course Epidemiology.
    Shaw C; Wu Y; Zimmerman SC; Hayes-Larson E; Belin TR; Power MC; Glymour MM; Mayeda ER
    Am J Epidemiol; 2023 Nov; 192(12):2075-2084. PubMed ID: 37338987
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 22.