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Journal Abstract Search

162 related items for PubMed ID: 36599049

  • 1. A Realistic Evaluation of Methods for Handling Missing Data When There is a Mixture of MCAR, MAR, and MNAR Mechanisms in the Same Dataset.
    Gomer B, Yuan KH.
    Multivariate Behav Res; 2023; 58(5):988-1013. PubMed ID: 36599049
    [Abstract] [Full Text] [Related]

  • 2. 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 19; 10():7. PubMed ID: 20085642
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  • 3. Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors.
    Galimard JE, Chevret S, Curis E, Resche-Rigon M.
    BMC Med Res Methodol; 2018 Aug 31; 18(1):90. PubMed ID: 30170561
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  • 4. A Bayesian Latent Variable Selection Model for Nonignorable Missingness.
    Du H, Enders C, Keller BT, Bradbury TN, Karney BR.
    Multivariate Behav Res; 2022 Aug 31; 57(2-3):478-512. PubMed ID: 33529056
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  • 5. A hybrid return to baseline imputation method to incorporate MAR and MNAR dropout missingness.
    Jin M.
    Contemp Clin Trials; 2022 Sep 31; 120():106859. PubMed ID: 35872135
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  • 6. Imputation of missing covariate in randomized controlled trials with a continuous outcome: Scoping review and new results.
    Kayembe MT, Jolani S, Tan FES, van Breukelen GJP.
    Pharm Stat; 2020 Nov 31; 19(6):840-860. PubMed ID: 32510791
    [Abstract] [Full Text] [Related]

  • 7. BayesMetab: treatment of missing values in metabolomic studies using a Bayesian modeling approach.
    Shah J, Brock GN, Gaskins J.
    BMC Bioinformatics; 2019 Dec 20; 20(Suppl 24):673. PubMed ID: 31861984
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  • 9. Do the methods used to analyse missing data really matter? An examination of data from an observational study of Intermediate Care patients.
    Kaambwa B, Bryan S, Billingham L.
    BMC Res Notes; 2012 Jun 27; 5():330. PubMed ID: 22738344
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  • 13. Maximum likelihood versus multiple imputation for missing data in small longitudinal samples with nonnormality.
    Shin T, Davison ML, Long JD.
    Psychol Methods; 2017 Sep 27; 22(3):426-449. PubMed ID: 27709974
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  • 15. Outcome-sensitive multiple imputation: a simulation study.
    Kontopantelis E, White IR, Sperrin M, Buchan I.
    BMC Med Res Methodol; 2017 Jan 09; 17(1):2. PubMed ID: 28068910
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  • 16. Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics.
    Dekermanjian JP, Shaddox E, Nandy D, Ghosh D, Kechris K.
    BMC Bioinformatics; 2022 May 16; 23(1):179. PubMed ID: 35578165
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  • 17. Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study.
    Sisk R, Sperrin M, Peek N, van Smeden M, Martin GP.
    Stat Methods Med Res; 2023 Aug 16; 32(8):1461-1477. PubMed ID: 37105540
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  • 18. Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review.
    Afkanpour M, Hosseinzadeh E, Tabesh H.
    BMC Med Res Methodol; 2024 Aug 28; 24(1):188. PubMed ID: 39198744
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  • 19. A Two-Step Approach for Analysis of Nonignorable Missing Outcomes in Longitudinal Regression: an Application to Upstate KIDS Study.
    Liu D, Yeung EH, McLain AC, Xie Y, Buck Louis GM, Sundaram R.
    Paediatr Perinat Epidemiol; 2017 Sep 28; 31(5):468-478. PubMed ID: 28767145
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