BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

147 related articles for article (PubMed ID: 37141446)

  • 1. SAS and R code for probabilistic quantitative bias analysis for misclassified binary variables and binary unmeasured confounders.
    Fox MP; MacLehose RF; Lash TL
    Int J Epidemiol; 2023 Oct; 52(5):1624-1633. PubMed ID: 37141446
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Quantitative bias analysis in practice: review of software for regression with unmeasured confounding.
    Kawabata E; Tilling K; Groenwold RHH; Hughes RA
    BMC Med Res Methodol; 2023 May; 23(1):111. PubMed ID: 37142961
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A systematic review of quantitative bias analysis applied to epidemiological research.
    Petersen JM; Ranker LR; Barnard-Mayers R; MacLehose RF; Fox MP
    Int J Epidemiol; 2021 Nov; 50(5):1708-1730. PubMed ID: 33880532
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A method to automate probabilistic sensitivity analyses of misclassified binary variables.
    Fox MP; Lash TL; Greenland S
    Int J Epidemiol; 2005 Dec; 34(6):1370-6. PubMed ID: 16172102
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Application of quantitative bias analysis for unmeasured confounding in cost-effectiveness modelling.
    Leahy TP; Duffield S; Kent S; Sammon C; Tzelis D; Ray J; Groenwold RH; Gomes M; Ramagopalan S; Grieve R
    J Comp Eff Res; 2022 Aug; 11(12):861-870. PubMed ID: 35678168
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study.
    Fewell Z; Davey Smith G; Sterne JA
    Am J Epidemiol; 2007 Sep; 166(6):646-55. PubMed ID: 17615092
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Multiple-bias Sensitivity Analysis Using Bounds.
    Smith LH; Mathur MB; VanderWeele TJ
    Epidemiology; 2021 Sep; 32(5):625-634. PubMed ID: 34224471
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Assessing the impact of unmeasured confounding for binary outcomes using confounding functions.
    Kasza J; Wolfe R; Schuster T
    Int J Epidemiol; 2017 Aug; 46(4):1303-1311. PubMed ID: 28338913
    [TBL] [Abstract][Full Text] [Related]  

  • 9. It's not all about residual confounding: a plea for QBA for epidemiologic researchers and educators.
    Fox MP; Adrien N; van Smeden M; Suarez E
    Am J Epidemiol; 2024 May; ():. PubMed ID: 38754869
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Assessing Techniques for Quantifying the Impact of Bias Due to an Unmeasured Confounder: An Applied Example.
    Barberio J; Ahern TP; MacLehose RF; Collin LJ; Cronin-Fenton DP; Damkier P; Sørensen HT; Lash TL
    Clin Epidemiol; 2021; 13():627-635. PubMed ID: 34349564
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable.
    Corbin M; Haslett S; Pearce N; Maule M; Greenland S
    Int J Epidemiol; 2017 Jun; 46(3):1063-1072. PubMed ID: 28338966
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Potential sensitivity of bias analysis results to incorrect assumptions of nondifferential or differential binary exposure misclassification.
    Johnson CY; Flanders WD; Strickland MJ; Honein MA; Howards PP
    Epidemiology; 2014 Nov; 25(6):902-9. PubMed ID: 25120106
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders.
    Vanderweele TJ; Arah OA
    Epidemiology; 2011 Jan; 22(1):42-52. PubMed ID: 21052008
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Quantitative bias analysis of prevalence under misclassification: evaluation indicators, calculation method and case analysis.
    Liu J; Wang S; Shao F
    Int J Epidemiol; 2023 Jun; 52(3):942-951. PubMed ID: 36625552
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Validity evaluation of indirect adjustment method for multiple unmeasured confounders: A simulation and empirical study.
    Byun G; Kim H; Kim SY; Kim SS; Oh H; Lee JT
    Environ Res; 2022 Mar; 204(Pt A):111992. PubMed ID: 34487697
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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; 2(2022):. PubMed ID: 36321557
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Investigation of the structure and magnitude of time-varying uncontrolled confounding in simulated cohort data analyzed using g-computation.
    Soohoo M; Arah OA
    Int J Epidemiol; 2023 Dec; 52(6):1907-1913. PubMed ID: 37898996
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Analysis approaches to address treatment nonadherence in pragmatic trials with point-treatment settings: a simulation study.
    Hossain MB; Mosquera L; Karim ME
    BMC Med Res Methodol; 2022 Feb; 22(1):46. PubMed ID: 35172746
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Bias Analysis Gone Bad.
    Lash TL; Ahern TP; Collin LJ; Fox MP; MacLehose RF
    Am J Epidemiol; 2021 Aug; 190(8):1604-1612. PubMed ID: 33778845
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Quantitative bias analysis for study and grant planning.
    Fox MP; Lash TL
    Ann Epidemiol; 2020 Mar; 43():32-36. PubMed ID: 32113733
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.