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Journal Abstract Search

240 related items for PubMed ID: 31304611

  • 1. Multiple imputation in the presence of an incomplete binary variable created from an underlying continuous variable.
    Grobler AC, Lee K.
    Biom J; 2020 Mar; 62(2):467-478. PubMed ID: 31304611
    [Abstract] [Full Text] [Related]

  • 2. A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study.
    De Silva AP, Moreno-Betancur M, De Livera AM, Lee KJ, Simpson JA.
    BMC Med Res Methodol; 2017 Jul 25; 17(1):114. PubMed ID: 28743256
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  • 4. 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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  • 6. Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model.
    Huque MH, Moreno-Betancur M, Quartagno M, Simpson JA, Carlin JB, Lee KJ.
    Biom J; 2020 Mar 06; 62(2):444-466. PubMed ID: 31919921
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  • 8. Multiple imputation for discrete data: Evaluation of the joint latent normal model.
    Quartagno M, Carpenter JR.
    Biom J; 2019 Jul 06; 61(4):1003-1019. PubMed ID: 30868652
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  • 9. 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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  • 10. 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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  • 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
    [Abstract] [Full Text] [Related]

  • 12. A comparison of multiple imputation methods for missing data in longitudinal studies.
    Huque MH, Carlin JB, Simpson JA, Lee KJ.
    BMC Med Res Methodol; 2018 Dec 12; 18(1):168. PubMed ID: 30541455
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  • 13. Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes.
    Lipsitz SR, Fitzmaurice GM, Weiss RD.
    Psychometrika; 2020 Dec 12; 85(4):890-904. PubMed ID: 33006740
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  • 15. Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model.
    Bartlett JW, Seaman SR, White IR, Carpenter JR, Alzheimer's Disease Neuroimaging Initiative*.
    Stat Methods Med Res; 2015 Aug 12; 24(4):462-87. PubMed ID: 24525487
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  • 16. Rounding strategies for multiply imputed binary data.
    Demirtas H.
    Biom J; 2009 Aug 12; 51(4):677-88. PubMed ID: 19650057
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  • 17. 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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  • 18. Logistic regression vs. predictive mean matching for imputing binary covariates.
    Austin PC, van Buuren S.
    Stat Methods Med Res; 2023 Nov 30; 32(11):2172-2183. PubMed ID: 37750213
    [Abstract] [Full Text] [Related]

  • 19. 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 30; 29(8):2074-2086. PubMed ID: 31680633
    [Abstract] [Full Text] [Related]

  • 20. Bias and Precision of the "Multiple Imputation, Then Deletion" Method for Dealing With Missing Outcome Data.
    Sullivan TR, Salter AB, Ryan P, Lee KJ.
    Am J Epidemiol; 2015 Sep 15; 182(6):528-34. PubMed ID: 26337075
    [Abstract] [Full Text] [Related]

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