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Journal Abstract Search

157 related items for PubMed ID: 31997388

  • 1. 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
    [Abstract] [Full Text] [Related]

  • 2. 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 15; 179(6):764-74. PubMed ID: 24589914
    [Abstract] [Full Text] [Related]

  • 3. MISL: Multiple imputation by super learning.
    Carpenito T, Manjourides J.
    Stat Methods Med Res; 2022 Oct 15; 31(10):1904-1915. PubMed ID: 35658622
    [Abstract] [Full Text] [Related]

  • 4. 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
    [Abstract] [Full Text] [Related]

  • 5. Logistic regression vs. predictive mean matching for imputing binary covariates.
    Austin PC, van Buuren S.
    Stat Methods Med Res; 2023 Nov 19; 32(11):2172-2183. PubMed ID: 37750213
    [Abstract] [Full Text] [Related]

  • 6. A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis.
    Jahangiri M, Kazemnejad A, Goldfeld KS, Daneshpour MS, Mostafaei S, Khalili D, Moghadas MR, Akbarzadeh M.
    BMC Med Res Methodol; 2023 Jul 06; 23(1):161. PubMed ID: 37415114
    [Abstract] [Full Text] [Related]

  • 7. Multiple imputation for longitudinal data using Bayesian lasso imputation model.
    Yamaguchi Y, Yoshida S, Misumi T, Maruo K.
    Stat Med; 2022 Mar 15; 41(6):1042-1058. PubMed ID: 35064581
    [Abstract] [Full Text] [Related]

  • 8. 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
    [Abstract] [Full Text] [Related]

  • 9. A comparison of existing methods for multiple imputation in individual participant data meta-analysis.
    Kunkel D, Kaizar EE.
    Stat Med; 2017 Sep 30; 36(22):3507-3532. PubMed ID: 28695667
    [Abstract] [Full Text] [Related]

  • 10. 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
    [Abstract] [Full Text] [Related]

  • 11. Appropriate inclusion of interactions was needed to avoid bias in multiple imputation.
    Tilling K, Williamson EJ, Spratt M, Sterne JA, Carpenter JR.
    J Clin Epidemiol; 2016 Dec 06; 80():107-115. PubMed ID: 27445178
    [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 06; 30(12):2685-2700. PubMed ID: 34643465
    [Abstract] [Full Text] [Related]

  • 13. Non-parametric approach for frequentist multiple imputation in survival analysis with missing covariates.
    Takeuchi Y, Ogawa M, Hagiwara Y, Matsuyama Y.
    Stat Methods Med Res; 2021 Jul 06; 30(7):1691-1707. PubMed ID: 34110942
    [Abstract] [Full Text] [Related]

  • 14. 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 06; 32(3):330-340. PubMed ID: 36380400
    [Abstract] [Full Text] [Related]

  • 15. Multiple imputation with sequential penalized regression.
    Zahid FM, Heumann C.
    Stat Methods Med Res; 2019 May 06; 28(5):1311-1327. PubMed ID: 29451087
    [Abstract] [Full Text] [Related]

  • 16. Multiple imputation by chained equations for systematically and sporadically missing multilevel data.
    Resche-Rigon M, White IR.
    Stat Methods Med Res; 2018 Jun 06; 27(6):1634-1649. PubMed ID: 27647809
    [Abstract] [Full Text] [Related]

  • 17. Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study.
    De Silva AP, Moreno-Betancur M, De Livera AM, Lee KJ, Simpson JA.
    BMC Med Res Methodol; 2019 Jan 10; 19(1):14. PubMed ID: 30630434
    [Abstract] [Full Text] [Related]

  • 18. A bias-corrected estimator in multiple imputation for missing data.
    Tomita H, Fujisawa H, Henmi M.
    Stat Med; 2018 Oct 15; 37(23):3373-3386. PubMed ID: 29845646
    [Abstract] [Full Text] [Related]

  • 19. Estimating the Prevalence of Injection Drug Use Among Acute Hepatitis C Cases From a National Surveillance System: Application of Random Forest-Based Multiple Imputation.
    Yin S, Ly KN, Barker LK, Bixler D, Thompson ND, Gupta N.
    J Public Health Manag Pract; 2018 Oct 15; 30(5):733-743. PubMed ID: 39041767
    [Abstract] [Full Text] [Related]

  • 20. 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
    [Abstract] [Full Text] [Related]

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