BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

537 related articles for article (PubMed ID: 25063043)

  • 1. Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis.
    MacKinnon DP; Pirlott AG
    Pers Soc Psychol Rev; 2015 Feb; 19(1):30-43. PubMed ID: 25063043
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Unifying instrumental variable and inverse probability weighting approaches for inference of causal treatment effect and unmeasured confounding in observational studies.
    Liu T; Hogan JW
    Stat Methods Med Res; 2021 Mar; 30(3):671-686. PubMed ID: 33213292
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Sensitivity plots for confounder bias in the single mediator model.
    Cox MG; Kisbu-Sakarya Y; Miočević M; MacKinnon DP
    Eval Rev; 2013 Oct; 37(5):405-31. PubMed ID: 24681690
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Classical and causal inference approaches to statistical mediation analysis.
    Ato García M; Vallejo Seco G; Ato Lozano E
    Psicothema; 2014 May; 26(2):252-9. PubMed ID: 24755028
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Effect decomposition in the presence of an exposure-induced mediator-outcome confounder.
    Vanderweele TJ; Vansteelandt S; Robins JM
    Epidemiology; 2014 Mar; 25(2):300-6. PubMed ID: 24487213
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Confounding in statistical mediation analysis: What it is and how to address it.
    Valente MJ; Pelham WE; Smyth H; MacKinnon DP
    J Couns Psychol; 2017 Nov; 64(6):659-671. PubMed ID: 29154577
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A review of causal estimation of effects in mediation analyses.
    Ten Have TR; Joffe MM
    Stat Methods Med Res; 2012 Feb; 21(1):77-107. PubMed ID: 21163849
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Instrumental variable methods for causal inference.
    Baiocchi M; Cheng J; Small DS
    Stat Med; 2014 Jun; 33(13):2297-340. PubMed ID: 24599889
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Causal Diagrams: Pitfalls and Tips.
    Suzuki E; Shinozaki T; Yamamoto E
    J Epidemiol; 2020 Apr; 30(4):153-162. PubMed ID: 32009103
    [TBL] [Abstract][Full Text] [Related]  

  • 10. An introduction to causal inference.
    Pearl J
    Int J Biostat; 2010 Feb; 6(2):Article 7. PubMed ID: 20305706
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment-induced intermediate confounding.
    Taguri M; Chiba Y
    Stat Med; 2015 Jan; 34(1):131-44. PubMed ID: 25312003
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Distribution-free mediation analysis for nonlinear models with confounding.
    Albert JM
    Epidemiology; 2012 Nov; 23(6):879-88. PubMed ID: 23007042
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Sensitivity Analysis Without Assumptions.
    Ding P; VanderWeele TJ
    Epidemiology; 2016 May; 27(3):368-77. PubMed ID: 26841057
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Causal mediation analysis on failure time outcome without sequential ignorability.
    Zheng C; Zhou XH
    Lifetime Data Anal; 2017 Oct; 23(4):533-559. PubMed ID: 27464958
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies.
    Liu SH; Ulbricht CM; Chrysanthopoulou SA; Lapane KL
    BMC Res Notes; 2016 Jul; 9():354. PubMed ID: 27439301
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Identification and robust estimation of swapped direct and indirect effects: Mediation analysis with unmeasured mediator-outcome confounding and intermediate confounding.
    Tai AS; Lin SH
    Stat Med; 2022 Sep; 41(21):4143-4158. PubMed ID: 35716042
    [TBL] [Abstract][Full Text] [Related]  

  • 17. The Assumptions of Direction Dependence Analysis.
    Thoemmes F
    Multivariate Behav Res; 2020; 55(4):516-522. PubMed ID: 31215241
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Assessing Omitted Confounder Bias in Multilevel Mediation Models.
    Tofighi D; Kelley K
    Multivariate Behav Res; 2016; 51(1):86-105. PubMed ID: 26881959
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies.
    Hogan JW; Lancaster T
    Stat Methods Med Res; 2004 Feb; 13(1):17-48. PubMed ID: 14746439
    [TBL] [Abstract][Full Text] [Related]  

  • 20. [Causal Inference in Medicine Part II. Directed acyclic graphs--a useful method for confounder selection, categorization of potential biases, and hypothesis specification].
    Suzuki E; Komatsu H; Yorifuji T; Yamamoto E; Doi H; Tsuda T
    Nihon Eiseigaku Zasshi; 2009 Sep; 64(4):796-805. PubMed ID: 19797848
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 27.