BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

219 related articles for article (PubMed ID: 32854161)

  • 21. Using instrumental variables to estimate a Cox's proportional hazards regression subject to additive confounding.
    MacKenzie TA; Tosteson TD; Morden NE; Stukel TA; O'Malley AJ
    Health Serv Outcomes Res Methodol; 2014 Jun; 14(1-2):54-68. PubMed ID: 25506259
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Confounder selection strategies targeting stable treatment effect estimators.
    Loh WW; Vansteelandt S
    Stat Med; 2021 Feb; 40(3):607-630. PubMed ID: 33150645
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Dose-response modeling in mental health using stein-like estimators with instrumental variables.
    Ginestet CE; Emsley R; Landau S
    Stat Med; 2017 May; 36(11):1696-1714. PubMed ID: 28222485
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Instrumental variables: application and limitations.
    Martens EP; Pestman WR; de Boer A; Belitser SV; Klungel OH
    Epidemiology; 2006 May; 17(3):260-7. PubMed ID: 16617274
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Confounding and regression adjustment in difference-in-differences studies.
    Zeldow B; Hatfield LA
    Health Serv Res; 2021 Oct; 56(5):932-941. PubMed ID: 33978956
    [TBL] [Abstract][Full Text] [Related]  

  • 26. On two-stage estimation of structural instrumental variable models.
    Choi BY; Fine JP; Brookhart MA
    Biometrika; 2017 Dec; 104(4):881-899. PubMed ID: 29430042
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses.
    Palmer TM; Thompson JR; Tobin MD; Sheehan NA; Burton PR
    Int J Epidemiol; 2008 Oct; 37(5):1161-8. PubMed ID: 18463132
    [TBL] [Abstract][Full Text] [Related]  

  • 28. A general approach to evaluating the bias of 2-stage instrumental variable estimators.
    Wan F; Small D; Mitra N
    Stat Med; 2018 May; 37(12):1997-2015. PubMed ID: 29572890
    [TBL] [Abstract][Full Text] [Related]  

  • 29. A causal proportional hazards estimator under homogeneous or heterogeneous selection in an IV setting.
    Sørensen DN; Martinussen T; Tchetgen Tchetgen E
    Lifetime Data Anal; 2019 Oct; 25(4):639-659. PubMed ID: 31065968
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Selection Bias When Estimating Average Treatment Effects Using One-sample Instrumental Variable Analysis.
    Hughes RA; Davies NM; Davey Smith G; Tilling K
    Epidemiology; 2019 May; 30(3):350-357. PubMed ID: 30896457
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Combating unmeasured confounding in cross-sectional studies: evaluating instrumental-variable and Heckman selection models.
    DeMaris A
    Psychol Methods; 2014 Sep; 19(3):380-97. PubMed ID: 25110904
    [TBL] [Abstract][Full Text] [Related]  

  • 32. 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]  

  • 33. A comparison of methods to estimate the survivor average causal effect in the presence of missing data: a simulation study.
    McGuinness MB; Kasza J; Karahalios A; Guymer RH; Finger RP; Simpson JA
    BMC Med Res Methodol; 2019 Dec; 19(1):223. PubMed ID: 31795945
    [TBL] [Abstract][Full Text] [Related]  

  • 34. A comparison of methods to handle skew distributed cost variables in the analysis of the resource consumption in schizophrenia treatment.
    Kilian R; Matschinger H; Löeffler W; Roick C; Angermeyer MC
    J Ment Health Policy Econ; 2002 Mar; 5(1):21-31. PubMed ID: 12529567
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Instrumental variable with competing risk model.
    Zheng C; Dai R; Hari PN; Zhang MJ
    Stat Med; 2017 Apr; 36(8):1240-1255. PubMed ID: 28064466
    [TBL] [Abstract][Full Text] [Related]  

  • 36. A comparison of confounder selection and adjustment methods for estimating causal effects using large healthcare databases.
    Benasseur I; Talbot D; Durand M; Holbrook A; Matteau A; Potter BJ; Renoux C; Schnitzer ME; Tarride JÉ; Guertin JR
    Pharmacoepidemiol Drug Saf; 2022 Apr; 31(4):424-433. PubMed ID: 34953160
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Bias Due to Confounders for the Exposure-Competing Risk Relationship.
    Lesko CR; Lau B
    Epidemiology; 2017 Jan; 28(1):20-27. PubMed ID: 27748680
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Valid instrumental variable selection method using negative control outcomes and constructing efficient estimator.
    Orihara S; Goto A; Taguri M
    Biom J; 2024 Jun; 66(4):e2300113. PubMed ID: 38801216
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Bespoke Instruments: A new tool for addressing unmeasured confounders.
    Richardson DB; Tchetgen Tchetgen EJ
    Am J Epidemiol; 2022 Mar; 191(5):939-947. PubMed ID: 34907434
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Adjustment for unmeasured confounding through informative priors for the confounder-outcome relation.
    Groenwold RHH; Shofty I; Miočević M; van Smeden M; Klugkist I
    BMC Med Res Methodol; 2018 Dec; 18(1):174. PubMed ID: 30577773
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 11.