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Journal Abstract Search

460 related items for PubMed ID: 24589914

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  • 2. A fair comparison of tree-based and parametric methods in multiple imputation by chained equations.
    Slade E, Naylor MG.
    Stat Med; 2020 Apr 15; 39(8):1156-1166. PubMed ID: 31997388
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  • 4. Performance of Multiple Imputation Using Modern Machine Learning Methods in Electronic Health Records Data.
    Getz K, Hubbard RA, Linn KA.
    Epidemiology; 2023 Mar 01; 34(2):206-215. PubMed ID: 36722803
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  • 5. SuperMICE: An Ensemble Machine Learning Approach to Multiple Imputation by Chained Equations.
    Laqueur HS, Shev AB, Kagawa RMC.
    Am J Epidemiol; 2022 Feb 19; 191(3):516-525. PubMed ID: 34788362
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  • 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 23; 19(1):161. PubMed ID: 31345166
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  • 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 06; 17(1):134. PubMed ID: 28877666
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  • 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 25; 20(1):199. PubMed ID: 32711455
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  • 13. Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random.
    Curnow E, Cornish RP, Heron JE, Carpenter JR, Tilling K.
    BMC Med Res Methodol; 2024 Oct 07; 24(1):231. PubMed ID: 39375597
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  • 15. MISL: Multiple imputation by super learning.
    Carpenito T, Manjourides J.
    Stat Methods Med Res; 2022 Oct 07; 31(10):1904-1915. PubMed ID: 35658622
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