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Journal Abstract Search

289 related items for PubMed ID: 24766825

  • 1. Comparison of methods for imputing limited-range variables: a simulation study.
    Rodwell L, Lee KJ, Romaniuk H, Carlin JB.
    BMC Med Res Methodol; 2014 Apr 26; 14():57. PubMed ID: 24766825
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  • 4. The ability of different imputation methods for missing values in mental measurement questionnaires.
    Xu X, Xia L, Zhang Q, Wu S, Wu M, Liu H.
    BMC Med Res Methodol; 2020 Feb 27; 20(1):42. PubMed ID: 32103723
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  • 5. Comparison of methods for imputing ordinal data using multivariate normal imputation: a case study of non-linear effects in a large cohort study.
    Lee KJ, Galati JC, Simpson JA, Carlin JB.
    Stat Med; 2012 Dec 30; 31(30):4164-74. PubMed ID: 22826110
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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. Dealing with missing data in a multi-question depression scale: a comparison of imputation methods.
    Shrive FM, Stuart H, Quan H, Ghali WA.
    BMC Med Res Methodol; 2006 Dec 13; 6():57. PubMed ID: 17166270
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  • 10. Multiple imputation for non-response when estimating HIV prevalence using survey data.
    Chinomona A, Mwambi H.
    BMC Public Health; 2015 Oct 16; 15():1059. PubMed ID: 26475303
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  • 11. Multiple imputation of semi-continuous exposure variables that are categorized for analysis.
    Nguyen CD, Moreno-Betancur M, Rodwell L, Romaniuk H, Carlin JB, Lee KJ.
    Stat Med; 2021 Nov 30; 40(27):6093-6106. PubMed ID: 34423450
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  • 12. Diagnosing problems with imputation models using the Kolmogorov-Smirnov test: a simulation study.
    Nguyen CD, Carlin JB, Lee KJ.
    BMC Med Res Methodol; 2013 Nov 20; 13():144. PubMed ID: 24252653
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  • 14. 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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  • 15. 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
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  • 16. Does pattern mixture modelling reduce bias due to informative attrition compared to fitting a mixed effects model to the available cases or data imputed using multiple imputation?: a simulation study.
    Welch CA, Sabia S, Brunner E, Kivimäki M, Shipley MJ.
    BMC Med Res Methodol; 2018 Aug 29; 18(1):89. PubMed ID: 30157752
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  • 17. A real data-driven simulation strategy to select an imputation method for mixed-type trait data.
    May JA, Feng Z, Adamowicz SJ.
    PLoS Comput Biol; 2023 Mar 29; 19(3):e1010154. PubMed ID: 36947561
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  • 18. 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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  • 19. Estimating range of influence in case of missing spatial data: a simulation study on binary data.
    Bihrmann K, Ersbøll AK.
    Int J Health Geogr; 2015 Jan 06; 14():1. PubMed ID: 25563056
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