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 *

1692 related articles for article (PubMed ID: 14746439)

  • 1. 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]  

  • 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. Assessing causal treatment effect estimation when using large observational datasets.
    John ER; Abrams KR; Brightling CE; Sheehan NA
    BMC Med Res Methodol; 2019 Nov; 19(1):207. PubMed ID: 31726969
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Instruments for causal inference: an epidemiologist's dream?
    Hernán MA; Robins JM
    Epidemiology; 2006 Jul; 17(4):360-72. PubMed ID: 16755261
    [TBL] [Abstract][Full Text] [Related]  

  • 5. The productivity of mental health care: an instrumental variable approach.
    Lu M
    J Ment Health Policy Econ; 1999 Jun; 2(2):59-71. PubMed ID: 11967410
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Estimating causal treatment effects from longitudinal HIV natural history studies using marginal structural models.
    Ko H; Hogan JW; Mayer KH
    Biometrics; 2003 Mar; 59(1):152-62. PubMed ID: 12762452
    [TBL] [Abstract][Full Text] [Related]  

  • 7. On a preference-based instrumental variable approach in reducing unmeasured confounding-by-indication.
    Li Y; Lee Y; Wolfe RA; Morgenstern H; Zhang J; Port FK; Robinson BM
    Stat Med; 2015 Mar; 34(7):1150-68. PubMed ID: 25546152
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures.
    Hernán MA; Brumback BA; Robins JM
    Stat Med; 2002 Jun; 21(12):1689-709. PubMed ID: 12111906
    [TBL] [Abstract][Full Text] [Related]  

  • 9. High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions.
    Neugebauer R; Schmittdiel JA; Zhu Z; Rassen JA; Seeger JD; Schneeweiss S
    Stat Med; 2015 Feb; 34(5):753-81. PubMed ID: 25488047
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Inverse probability weighting in sexually transmitted infection/human immunodeficiency virus prevention research: methods for evaluating social and community interventions.
    Lippman SA; Shade SB; Hubbard AE
    Sex Transm Dis; 2010 Aug; 37(8):512-8. PubMed ID: 20375927
    [TBL] [Abstract][Full Text] [Related]  

  • 11. The impact of unmeasured within- and between-cluster confounding on the bias of effect estimatorsof a continuous exposure.
    Li Y; Lee Y; Port FK; Robinson BM
    Stat Methods Med Res; 2020 Aug; 29(8):2119-2139. PubMed ID: 31694489
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Instrumental Variable Analyses and Selection Bias.
    Canan C; Lesko C; Lau B
    Epidemiology; 2017 May; 28(3):396-398. PubMed ID: 28169934
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A tutorial on the use of instrumental variables in pharmacoepidemiology.
    Ertefaie A; Small DS; Flory JH; Hennessy S
    Pharmacoepidemiol Drug Saf; 2017 Apr; 26(4):357-367. PubMed ID: 28239929
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Two-stage instrumental variable methods for estimating the causal odds ratio: analysis of bias.
    Cai B; Small DS; Have TR
    Stat Med; 2011 Jul; 30(15):1809-24. PubMed ID: 21495062
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Sensitivity analyses for unmeasured confounding assuming a marginal structural model for repeated measures.
    Brumback BA; Hernán MA; Haneuse SJ; Robins JM
    Stat Med; 2004 Mar; 23(5):749-67. PubMed ID: 14981673
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Correcting for Measurement Error in Time-Varying Covariates in Marginal Structural Models.
    Kyle RP; Moodie EE; Klein MB; Abrahamowicz M
    Am J Epidemiol; 2016 Aug; 184(3):249-58. PubMed ID: 27416840
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Time dependent hazard ratio estimation using instrumental variables without conditioning on an omitted covariate.
    MacKenzie TA; Martinez-Camblor P; O'Malley AJ
    BMC Med Res Methodol; 2021 Mar; 21(1):56. PubMed ID: 33743583
    [TBL] [Abstract][Full Text] [Related]  

  • 19. History-adjusted marginal structural models for estimating time-varying effect modification.
    Petersen ML; Deeks SG; Martin JN; van der Laan MJ
    Am J Epidemiol; 2007 Nov; 166(9):985-93. PubMed ID: 17875580
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 85.