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197 related items for PubMed ID: 38567439

  • 1. Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials.
    Blette BS, Halpern SD, Li F, Harhay MO.
    Stat Methods Med Res; 2024 May; 33(5):909-927. PubMed ID: 38567439
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  • 5. Multiple imputation methods for bivariate outcomes in cluster randomised trials.
    DiazOrdaz K, Kenward MG, Gomes M, Grieve R.
    Stat Med; 2016 Sep 10; 35(20):3482-96. PubMed ID: 26990655
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  • 7. Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials.
    Tong G, Tong J, Jiang Y, Esserman D, Harhay MO, Warren JL.
    Clin Trials; 2024 Aug 10; 21(4):451-460. PubMed ID: 38197388
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  • 8. Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity.
    Tong J, Li F, Harhay MO, Tong G.
    BMC Med Res Methodol; 2023 Apr 06; 23(1):85. PubMed ID: 37024809
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  • 10. Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.
    Hossain A, Diaz-Ordaz K, Bartlett JW.
    Stat Methods Med Res; 2017 Jun 06; 26(3):1543-1562. PubMed ID: 27177885
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  • 13. A pattern-mixture model approach for handling missing continuous outcome data in longitudinal cluster randomized trials.
    Fiero MH, Hsu CH, Bell ML.
    Stat Med; 2017 Nov 20; 36(26):4094-4105. PubMed ID: 28783884
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  • 14. 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 20; 25(6):2650-2669. PubMed ID: 24713160
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  • 15. 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 20; 19(6):840-860. PubMed ID: 32510791
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  • 16. The mixed model for repeated measures for cluster randomized trials: a simulation study investigating bias and type I error with missing continuous data.
    Bell ML, Rabe BA.
    Trials; 2020 Feb 07; 21(1):148. PubMed ID: 32033617
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  • 17. 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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  • 18. 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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  • 19. Intent-to-treat analysis of cluster randomized trials when clusters report unidentifiable outcome proportions.
    DeSantis SM, Li R, Zhang Y, Wang X, Vernon SW, Tilley BC, Koch G.
    Clin Trials; 2020 Dec 15; 17(6):627-636. PubMed ID: 32838555
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  • 20. 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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