BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

437 related articles for article (PubMed ID: 32510791)

  • 1. 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; 19(6):840-860. PubMed ID: 32510791
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Imputation of Missing Covariates in Randomized Controlled Trials with Continuous Outcomes: Simple, Unbiased and Efficient Methods.
    Kayembe MT; Jolani S; Tan FES; van Breukelen GJP
    J Biopharm Stat; 2022 Sep; 32(5):717-739. PubMed ID: 35041565
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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; 17():341. PubMed ID: 27450066
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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; 10():7. PubMed ID: 20085642
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Outcome-sensitive multiple imputation: a simulation study.
    Kontopantelis E; White IR; Sperrin M; Buchan I
    BMC Med Res Methodol; 2017 Jan; 17(1):2. PubMed ID: 28068910
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Imputation strategies for missing binary outcomes in cluster randomized trials.
    Ma J; Akhtar-Danesh N; Dolovich L; Thabane L;
    BMC Med Res Methodol; 2011 Feb; 11():18. PubMed ID: 21324148
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Dealing with missing outcome data in randomized trials and observational studies.
    Groenwold RH; Donders AR; Roes KC; Harrell FE; Moons KG
    Am J Epidemiol; 2012 Feb; 175(3):210-7. PubMed ID: 22262640
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.
    Hossain A; Diaz-Ordaz K; Bartlett JW
    Stat Methods Med Res; 2017 Jun; 26(3):1543-1562. PubMed ID: 27177885
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Approaches for missing covariate data in logistic regression with MNAR sensitivity analyses.
    Ward RC; Axon RN; Gebregziabher M
    Biom J; 2020 Jul; 62(4):1025-1037. PubMed ID: 31957905
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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; 17(1):114. PubMed ID: 28743256
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Covariate adjustment in randomized clinical trials with missing covariate and outcome data.
    Chang CR; Song Y; Li F; Wang R
    Stat Med; 2023 Sep; 42(22):3919-3935. PubMed ID: 37394874
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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; 29(5):1338-1353. PubMed ID: 31293199
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Evaluation of multiple imputation approaches for handling missing covariate information in a case-cohort study with a binary outcome.
    Middleton M; Nguyen C; Moreno-Betancur M; Carlin JB; Lee KJ
    BMC Med Res Methodol; 2022 Apr; 22(1):87. PubMed ID: 35369860
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.
    White IR; Carlin JB
    Stat Med; 2010 Dec; 29(28):2920-31. PubMed ID: 20842622
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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; 33(8):1051-63. PubMed ID: 23913915
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review.
    Mainzer R; Moreno-Betancur M; Nguyen C; Simpson J; Carlin J; Lee K
    BMJ Open; 2023 Feb; 13(2):e065576. PubMed ID: 36725096
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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; 182(6):528-34. PubMed ID: 26337075
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A multiple imputation-based sensitivity analysis approach for regression analysis with a missing not at random covariate.
    Hsu CH; He Y; Hu C; Zhou W
    Stat Med; 2023 Jun; 42(14):2275-2292. PubMed ID: 36997162
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Missing binary outcomes under covariate-dependent missingness in cluster randomised trials.
    Hossain A; DiazOrdaz K; Bartlett JW
    Stat Med; 2017 Aug; 36(19):3092-3109. PubMed ID: 28557022
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A hybrid return to baseline imputation method to incorporate MAR and MNAR dropout missingness.
    Jin M
    Contemp Clin Trials; 2022 Sep; 120():106859. PubMed ID: 35872135
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 22.