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 *

440 related articles for article (PubMed ID: 28239929)

  • 1. 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]  

  • 2. The missing cause approach to unmeasured confounding in pharmacoepidemiology.
    Abrahamowicz M; Bjerre LM; Beauchamp ME; LeLorier J; Burne R
    Stat Med; 2016 Mar; 35(7):1001-16. PubMed ID: 26932124
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Unmeasured confounding in pharmacoepidemiology.
    Groenwold RH; de Groot MC; Ramamoorthy D; Souverein PC; Klungel OH
    Ann Epidemiol; 2016 Jan; 26(1):85-6. PubMed ID: 26559329
    [No Abstract]   [Full Text] [Related]  

  • 4. Simulation study of instrumental variable approaches with an application to a study of the antidiabetic effect of bezafibrate.
    Cai B; Hennessy S; Flory JH; Sha D; Ten Have TR; Small DS
    Pharmacoepidemiol Drug Saf; 2012 May; 21 Suppl 2():114-20. PubMed ID: 22552986
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Martingale residual-based method to control for confounders measured only in a validation sample in time-to-event analysis.
    Burne RM; Abrahamowicz M
    Stat Med; 2016 Nov; 35(25):4588-4606. PubMed ID: 27306611
    [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. 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]  

  • 9. Performance of instrumental variable methods in cohort and nested case-control studies: a simulation study.
    Uddin MJ; Groenwold RH; de Boer A; Belitser SV; Roes KC; Hoes AW; Klungel OH
    Pharmacoepidemiol Drug Saf; 2014 Feb; 23(2):165-77. PubMed ID: 24306965
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Methods to control for unmeasured confounding in pharmacoepidemiology: an overview.
    Uddin MJ; Groenwold RH; Ali MS; de Boer A; Roes KC; Chowdhury MA; Klungel OH
    Int J Clin Pharm; 2016 Jun; 38(3):714-23. PubMed ID: 27091131
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration.
    Stürmer T; Schneeweiss S; Avorn J; Glynn RJ
    Am J Epidemiol; 2005 Aug; 162(3):279-89. PubMed ID: 15987725
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Sensitivity analysis and power for instrumental variable studies.
    Wang X; Jiang Y; Zhang NR; Small DS
    Biometrics; 2018 Dec; 74(4):1150-1160. PubMed ID: 29603714
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Probabilistic bias analysis in pharmacoepidemiology and comparative effectiveness research: a systematic review.
    Hunnicutt JN; Ulbricht CM; Chrysanthopoulou SA; Lapane KL
    Pharmacoepidemiol Drug Saf; 2016 Dec; 25(12):1343-1353. PubMed ID: 27593968
    [TBL] [Abstract][Full Text] [Related]  

  • 16. "A Bayesian sensitivity analysis to evaluate the impact of unmeasured confounding with external data: a real world comparative effectiveness study in osteoporosis".
    Zhang X; Faries DE; Boytsov N; Stamey JD; Seaman JW
    Pharmacoepidemiol Drug Saf; 2016 Sep; 25(9):982-92. PubMed ID: 27396534
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. Addressing unmeasured confounding in comparative observational research.
    Zhang X; Faries DE; Li H; Stamey JD; Imbens GW
    Pharmacoepidemiol Drug Saf; 2018 Apr; 27(4):373-382. PubMed ID: 29383840
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Sensitivity analysis of G-estimators to invalid instrumental variables.
    Vancak V; Sjölander A
    Stat Med; 2023 Oct; 42(23):4257-4281. PubMed ID: 37497859
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 22.