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Journal Abstract Search

721 related items for PubMed ID: 21324148

  • 1. Imputation strategies for missing binary outcomes in cluster randomized trials.
    Ma J, Akhtar-Danesh N, Dolovich L, Thabane L, CHAT investigators.
    BMC Med Res Methodol; 2011 Feb 16; 11():18. PubMed ID: 21324148
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  • 2. Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study.
    Ma J, Raina P, Beyene J, Thabane L.
    BMC Med Res Methodol; 2013 Jan 23; 13():9. PubMed ID: 23343209
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  • 3. Comparison of Bayesian and classical methods in the analysis of cluster randomized controlled trials with a binary outcome: the Community Hypertension Assessment Trial (CHAT).
    Ma J, Thabane L, Kaczorowski J, Chambers L, Dolovich L, Karwalajtys T, Levitt C.
    BMC Med Res Methodol; 2009 Jun 16; 9():37. PubMed ID: 19531226
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  • 4. Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness.
    Turner EL, Yao L, Li F, Prague M.
    Stat Methods Med Res; 2020 May 16; 29(5):1338-1353. PubMed ID: 31293199
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  • 5. 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
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  • 6. Multiple imputation methods for handling missing data in cost-effectiveness analyses that use data from hierarchical studies: an application to cluster randomized trials.
    Gomes M, Díaz-Ordaz K, Grieve R, Kenward MG.
    Med Decis Making; 2013 Nov 15; 33(8):1051-63. PubMed ID: 23913915
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  • 8. Imputation strategies for missing continuous outcomes in cluster randomized trials.
    Taljaard M, Donner A, Klar N.
    Biom J; 2008 Jun 15; 50(3):329-45. PubMed ID: 18537126
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  • 9. A comparison of imputation strategies in cluster randomized trials with missing binary outcomes.
    Caille A, Leyrat C, Giraudeau B.
    Stat Methods Med Res; 2016 Dec 15; 25(6):2650-2669. PubMed ID: 24713160
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  • 10. Missing binary outcomes under covariate-dependent missingness in cluster randomised trials.
    Hossain A, DiazOrdaz K, Bartlett JW.
    Stat Med; 2017 Aug 30; 36(19):3092-3109. PubMed ID: 28557022
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  • 11. Using Multiple Imputation with GEE with Non-monotone Missing Longitudinal Binary Outcomes.
    Lipsitz SR, Fitzmaurice GM, Weiss RD.
    Psychometrika; 2020 Dec 30; 85(4):890-904. PubMed ID: 33006740
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  • 13. Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?
    Mukaka M, White SA, Terlouw DJ, Mwapasa V, Kalilani-Phiri L, Faragher EB.
    Trials; 2016 Jul 22; 17():341. PubMed ID: 27450066
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  • 17. A readily available improvement over method of moments for intra-cluster correlation estimation in the context of cluster randomized trials and fitting a GEE-type marginal model for binary outcomes.
    Westgate PM.
    Clin Trials; 2019 Feb 22; 16(1):41-51. PubMed ID: 30295512
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