These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

175 related articles for article (PubMed ID: 17703496)

  • 1. Allowing for uncertainty due to missing data in meta-analysis--part 1: two-stage methods.
    White IR; Higgins JP; Wood AM
    Stat Med; 2008 Feb; 27(5):711-27. PubMed ID: 17703496
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Allowing for uncertainty due to missing data in meta-analysis--part 2: hierarchical models.
    White IR; Welton NJ; Wood AM; Ades AE; Higgins JP
    Stat Med; 2008 Feb; 27(5):728-45. PubMed ID: 17703502
    [TBL] [Abstract][Full Text] [Related]  

  • 3. An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis.
    Spineli LM
    BMC Med Res Methodol; 2019 Apr; 19(1):86. PubMed ID: 31018836
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Evaluating the impact of imputations for missing participant outcome data in a network meta-analysis.
    Spineli LM; Higgins JP; Cipriani A; Leucht S; Salanti G
    Clin Trials; 2013; 10(3):378-88. PubMed ID: 23321265
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Potential impact of missing outcome data on treatment effects in systematic reviews: imputation study.
    Kahale LA; Khamis AM; Diab B; Chang Y; Lopes LC; Agarwal A; Li L; Mustafa RA; Koujanian S; Waziry R; Busse JW; Dakik A; Schünemann HJ; Hooft L; Scholten RJ; Guyatt GH; Akl EA
    BMJ; 2020 Aug; 370():m2898. PubMed ID: 32847800
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis.
    Mavridis D; White IR; Higgins JP; Cipriani A; Salanti G
    Stat Med; 2015 Feb; 34(5):721-41. PubMed ID: 25393541
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Participants' outcomes gone missing within a network of interventions: Bayesian modeling strategies.
    Spineli LM; Kalyvas C; Pateras K
    Stat Med; 2019 Sep; 38(20):3861-3879. PubMed ID: 31134664
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta-analysis.
    Turner NL; Dias S; Ades AE; Welton NJ
    Stat Med; 2015 May; 34(12):2062-80. PubMed ID: 25809313
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Dealing with missing outcome data in meta-analysis.
    Mavridis D; White IR
    Res Synth Methods; 2020 Jan; 11(1):2-13. PubMed ID: 30991455
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Imputation methods for missing outcome data in meta-analysis of clinical trials.
    Higgins JP; White IR; Wood AM
    Clin Trials; 2008; 5(3):225-39. PubMed ID: 18559412
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Accounting for uncertainty due to 'last observation carried forward' outcome imputation in a meta-analysis model.
    Dimitrakopoulou V; Efthimiou O; Leucht S; Salanti G
    Stat Med; 2015 Feb; 34(5):742-52. PubMed ID: 25492741
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Continuous(ly) missing outcome data in network meta-analysis: A one-stage pattern-mixture model approach.
    Spineli LM; Kalyvas C; Papadimitropoulou K
    Stat Methods Med Res; 2021 Apr; 30(4):958-975. PubMed ID: 33406990
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Eliciting and using expert opinions about dropout bias in randomized controlled trials.
    White IR; Carpenter J; Evans S; Schroter S
    Clin Trials; 2007; 4(2):125-39. PubMed ID: 17456512
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Addressing missing outcome data in meta-analysis.
    Mavridis D; Chaimani A; Efthimiou O; Leucht S; Salanti G
    Evid Based Ment Health; 2014 Aug; 17(3):85-9. PubMed ID: 25009175
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Allowing for uncertainty due to missing and LOCF imputed outcomes in meta-analysis.
    Mavridis D; Salanti G; Furukawa TA; Cipriani A; Chaimani A; White IR
    Stat Med; 2019 Feb; 38(5):720-737. PubMed ID: 30347460
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of exclusion, imputation and modelling of missing binary outcome data in frequentist network meta-analysis.
    Spineli LM; Kalyvas C
    BMC Med Res Methodol; 2020 Feb; 20(1):48. PubMed ID: 32111167
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Bayesian sensitivity analyses for longitudinal data with dropouts that are potentially missing not at random: A high dimensional pattern-mixture model.
    Kaciroti NA; Little RJA
    Stat Med; 2021 Sep; 40(21):4609-4628. PubMed ID: 34405912
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Bayesian hierarchical models for network meta-analysis incorporating nonignorable missingness.
    Zhang J; Chu H; Hong H; Virnig BA; Carlin BP
    Stat Methods Med Res; 2017 Oct; 26(5):2227-2243. PubMed ID: 26220535
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Problematic meta-analyses: Bayesian and frequentist perspectives on combining randomized controlled trials and non-randomized studies.
    Moran JL; Linden A
    BMC Med Res Methodol; 2024 Apr; 24(1):99. PubMed ID: 38678213
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Development of a practical approach to expert elicitation for randomised controlled trials with missing health outcomes: Application to the IMPROVE trial.
    Mason AJ; Gomes M; Grieve R; Ulug P; Powell JT; Carpenter J
    Clin Trials; 2017 Aug; 14(4):357-367. PubMed ID: 28675302
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.