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 *

443 related articles for article (PubMed ID: 33308213)

  • 1. Can statistical adjustment guided by causal inference improve the accuracy of effect estimation? A simulation and empirical research based on meta-analyses of case-control studies.
    Yan R; Liu T; Peng Y; Peng X
    BMC Med Inform Decis Mak; 2020 Dec; 20(1):333. PubMed ID: 33308213
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Can statistic adjustment of OR minimize the potential confounding bias for meta-analysis of case-control study? A secondary data analysis.
    Liu T; Nie X; Wu Z; Zhang Y; Feng G; Cai S; Lv Y; Peng X
    BMC Med Res Methodol; 2017 Dec; 17(1):179. PubMed ID: 29284414
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A simulation study on matched case-control designs in the perspective of causal diagrams.
    Li H; Yuan Z; Su P; Wang T; Yu Y; Sun X; Xue F
    BMC Med Res Methodol; 2016 Aug; 16(1):102. PubMed ID: 27543263
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. COVID-19 and the epistemology of epidemiological models at the dawn of AI.
    Ellison GTH
    Ann Hum Biol; 2020 Sep; 47(6):506-513. PubMed ID: 33228409
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models.
    Kelter R
    BMC Med Res Methodol; 2022 Feb; 22(1):58. PubMed ID: 35220960
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.
    Malec SA; Wei P; Bernstam EV; Boyce RD; Cohen T
    J Biomed Inform; 2021 May; 117():103719. PubMed ID: 33716168
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Validity evaluation of indirect adjustment method for multiple unmeasured confounders: A simulation and empirical study.
    Byun G; Kim H; Kim SY; Kim SS; Oh H; Lee JT
    Environ Res; 2022 Mar; 204(Pt A):111992. PubMed ID: 34487697
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Comparison of the ability of double-robust estimators to correct bias in propensity score matching analysis. A Monte Carlo simulation study.
    Nguyen TL; Collins GS; Spence J; Devereaux PJ; Daurès JP; Landais P; Le Manach Y
    Pharmacoepidemiol Drug Saf; 2017 Dec; 26(12):1513-1519. PubMed ID: 28984050
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. When does measurement error in covariates impact causal effect estimates? Analytic derivations of different scenarios and an empirical illustration.
    Sengewald MA; Steiner PM; Pohl S
    Br J Math Stat Psychol; 2019 May; 72(2):244-270. PubMed ID: 30345554
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation.
    Genbäck M; de Luna X
    Biometrics; 2019 Jun; 75(2):506-515. PubMed ID: 30430543
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Directed Acyclic Graphs for Oral Disease Research.
    Akinkugbe AA; Sharma S; Ohrbach R; Slade GD; Poole C
    J Dent Res; 2016 Jul; 95(8):853-9. PubMed ID: 27000052
    [TBL] [Abstract][Full Text] [Related]  

  • 15. [Application of directed acyclic graphs in identifying and controlling confounding bias].
    Liu HX; Wang HB; Wang N
    Zhonghua Liu Xing Bing Xue Za Zhi; 2020 Apr; 41(4):585-588. PubMed ID: 32344486
    [TBL] [Abstract][Full Text] [Related]  

  • 16. [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]  

  • 17. 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]  

  • 18. The Janus face of statistical adjustment: confounders versus colliders.
    Janszky I; Ahlbom A; Svensson AC
    Eur J Epidemiol; 2010 Jun; 25(6):361-3. PubMed ID: 20449636
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Interpreting epidemiological evidence: how meta-analysis and causal inference methods are related.
    Weed DL
    Int J Epidemiol; 2000 Jun; 29(3):387-90. PubMed ID: 10869307
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Should we adjust for a confounder if empirical and theoretical criteria yield contradictory results? A simulation study.
    Lee PH
    Sci Rep; 2014 Aug; 4():6085. PubMed ID: 25124526
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 23.