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 *

171 related articles for article (PubMed ID: 33942919)

  • 1. Using generalized linear models to implement g-estimation for survival data with time-varying confounding.
    Seaman SR; Keogh RH; Dukes O; Vansteelandt S
    Stat Med; 2021 Jul; 40(16):3779-3790. PubMed ID: 33942919
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Adjusting for time-varying confounders in survival analysis using structural nested cumulative survival time models.
    Seaman S; Dukes O; Keogh R; Vansteelandt S
    Biometrics; 2020 Jun; 76(2):472-483. PubMed ID: 31562652
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips.
    Shinozaki T; Suzuki E
    J Epidemiol; 2020 Sep; 30(9):377-389. PubMed ID: 32684529
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Causal Methods for Observational Research: A Primer.
    Almasi-Hashiani A; Nedjat S; Mansournia MA
    Arch Iran Med; 2018 Apr; 21(4):164-169. PubMed ID: 29693407
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Augmented and doubly robust G-estimation of causal effects under a Structural nested failure time model.
    Mertens K; Vansteelandt S
    Biometrics; 2018 Jun; 74(2):472-480. PubMed ID: 28742252
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Causal models adjusting for time-varying confounding-a systematic review of the literature.
    Clare PJ; Dobbins TA; Mattick RP
    Int J Epidemiol; 2019 Feb; 48(1):254-265. PubMed ID: 30358847
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. G-estimation of structural nested restricted mean time lost models to estimate effects of time-varying treatments on a failure time outcome.
    Hagiwara Y; Shinozaki T; Matsuyama Y
    Biometrics; 2020 Sep; 76(3):799-810. PubMed ID: 31829432
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals.
    Almirall D; Griffin BA; McCaffrey DF; Ramchand R; Yuen RA; Murphy SA
    Stat Med; 2014 Sep; 33(20):3466-87. PubMed ID: 23873437
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.
    Yu Y; Li H; Sun X; Su P; Wang T; Liu Y; Yuan Z; Liu Y; Xue F
    BMC Med Res Methodol; 2017 Dec; 17(1):177. PubMed ID: 29281984
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Parametric g-formula for Testing Time-Varying Causal Effects: What It Is, Why It Matters, and How to Implement It in Lavaan.
    Loh WW; Ren D; West SG
    Multivariate Behav Res; 2024; 59(5):995-1018. PubMed ID: 38963381
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Estimating long-term treatment effects in observational data: A comparison of the performance of different methods under real-world uncertainty.
    Newsome SJ; Keogh RH; Daniel RM
    Stat Med; 2018 Jul; 37(15):2367-2390. PubMed ID: 29671915
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Structural Nested Cumulative Failure Time Models to Estimate the Effects of Interventions.
    Picciotto S; HernĂ¡n MA; Page JH; Young JG; Robins JM
    J Am Stat Assoc; 2012; 107(499):. PubMed ID: 24347749
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Structural equation modeling versus marginal structural modeling for assessing mediation in the presence of posttreatment confounding.
    Moerkerke B; Loeys T; Vansteelandt S
    Psychol Methods; 2015 Jun; 20(2):204-20. PubMed ID: 25751514
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Targeted learning in real-world comparative effectiveness research with time-varying interventions.
    Neugebauer R; Schmittdiel JA; van der Laan MJ
    Stat Med; 2014 Jun; 33(14):2480-520. PubMed ID: 24535915
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Marginal Structural Models: unbiased estimation for longitudinal studies.
    Moodie EE; Stephens DA
    Int J Public Health; 2011 Feb; 56(1):117-9. PubMed ID: 20931349
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Joint mixed-effects models for causal inference with longitudinal data.
    Shardell M; Ferrucci L
    Stat Med; 2018 Feb; 37(5):829-846. PubMed ID: 29205454
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods.
    Keogh RH; Daniel RM; VanderWeele TJ; Vansteelandt S
    Am J Epidemiol; 2018 May; 187(5):1085-1092. PubMed ID: 29020128
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An introduction to g methods.
    Naimi AI; Cole SR; Kennedy EH
    Int J Epidemiol; 2017 Apr; 46(2):756-762. PubMed ID: 28039382
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models.
    Keogh RH; Gran JM; Seaman SR; Davies G; Vansteelandt S
    Stat Med; 2023 Jun; 42(13):2191-2225. PubMed ID: 37086186
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.