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Journal Abstract Search

179 related items for PubMed ID: 37024809

  • 1. 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
    [Abstract] [Full Text] [Related]

  • 2. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
    Crider K, Williams J, Qi YP, Gutman J, Yeung L, Mai C, Finkelstain J, Mehta S, Pons-Duran C, Menéndez C, Moraleda C, Rogers L, Daniels K, Green P.
    Cochrane Database Syst Rev; 2022 Feb 01; 2(2022):. PubMed ID: 36321557
    [Abstract] [Full Text] [Related]

  • 3. 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
    [Abstract] [Full Text] [Related]

  • 4. Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes.
    Maleyeff L, Wang R, Haneuse S, Li F.
    Stat Med; 2023 Nov 30; 42(27):5054-5083. PubMed ID: 37974475
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  • 5. Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances.
    Tong G, Taljaard M, Li F.
    Stat Med; 2023 Aug 30; 42(19):3392-3412. PubMed ID: 37316956
    [Abstract] [Full Text] [Related]

  • 6. 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 30; 29(5):1338-1353. PubMed ID: 31293199
    [Abstract] [Full Text] [Related]

  • 7. Imputation strategies for missing continuous outcomes in cluster randomized trials.
    Taljaard M, Donner A, Klar N.
    Biom J; 2008 Jun 30; 50(3):329-45. PubMed ID: 18537126
    [Abstract] [Full Text] [Related]

  • 8. 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
    [Abstract] [Full Text] [Related]

  • 9. Power and sample size calculations for cluster randomized trials with binary outcomes when intracluster correlation coefficients vary by treatment arm.
    Kennedy-Shaffer L, Hughes MD.
    Clin Trials; 2022 Feb 07; 19(1):42-51. PubMed ID: 34879711
    [Abstract] [Full Text] [Related]

  • 10. Accounting for expected attrition in the planning of community intervention trials.
    Taljaard M, Donner A, Klar N.
    Stat Med; 2007 Jun 15; 26(13):2615-28. PubMed ID: 17068842
    [Abstract] [Full Text] [Related]

  • 11. Sample size calculation for randomized trials via inverse probability of response weighting when outcome data are missing at random.
    Harrison LJ, Wang R.
    Stat Med; 2023 May 20; 42(11):1802-1821. PubMed ID: 36880120
    [Abstract] [Full Text] [Related]

  • 12. 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 20; 33(5):909-927. PubMed ID: 38567439
    [Abstract] [Full Text] [Related]

  • 13. Impact of subject attrition on sample size determinations for longitudinal cluster randomized clinical trials.
    Heo M.
    J Biopharm Stat; 2014 May 20; 24(3):507-22. PubMed ID: 24697555
    [Abstract] [Full Text] [Related]

  • 14. The cluster randomized crossover trial: The effects of attrition in the AB/BA design and how to account for it in sample size calculations.
    Moerbeek M.
    Clin Trials; 2020 Aug 20; 17(4):420-429. PubMed ID: 32191129
    [Abstract] [Full Text] [Related]

  • 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
    [Abstract] [Full Text] [Related]

  • 16. 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
    [Abstract] [Full Text] [Related]

  • 17. Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs.
    Wang X, Chen X, Goldfeld KS, Taljaard M, Li F.
    Stat Methods Med Res; 2024 Jul 23; 33(7):1115-1136. PubMed ID: 38689556
    [Abstract] [Full Text] [Related]

  • 18. Sample size requirements for detecting treatment effect heterogeneity in cluster randomized trials.
    Yang S, Li F, Starks MA, Hernandez AF, Mentz RJ, Choudhury KR.
    Stat Med; 2020 Dec 10; 39(28):4218-4237. PubMed ID: 32823372
    [Abstract] [Full Text] [Related]

  • 19. Missing not at random models for masked clinical trials with dropouts.
    Kang S, Little RJ, Kaciroti N.
    Clin Trials; 2015 Apr 10; 12(2):139-48. PubMed ID: 25627429
    [Abstract] [Full Text] [Related]

  • 20. An imbalance in cluster sizes does not lead to notable loss of power in cross-sectional, stepped-wedge cluster randomised trials with a continuous outcome.
    Kristunas CA, Smith KL, Gray LJ.
    Trials; 2017 Mar 07; 18(1):109. PubMed ID: 28270224
    [Abstract] [Full Text] [Related]

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