BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

151 related articles for article (PubMed ID: 34225653)

  • 1. Noncollapsibility and its role in quantifying confounding bias in logistic regression.
    Schuster NA; Twisk JWR; Ter Riet G; Heymans MW; Rijnhart JJM
    BMC Med Res Methodol; 2021 Jul; 21(1):136. PubMed ID: 34225653
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Studying noncollapsibility of the odds ratio with marginal structural and logistic regression models.
    Pang M; Kaufman JS; Platt RW
    Stat Methods Med Res; 2016 Oct; 25(5):1925-1937. PubMed ID: 24108272
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Noncollapsibility in studies based on nonrepresentative samples.
    Pizzi C; Pearce N; Richiardi L
    Ann Epidemiol; 2015 Dec; 25(12):955-8. PubMed ID: 26603127
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Noncollapsibility, confounding, and sparse-data bias. Part 2: What should researchers make of persistent controversies about the odds ratio?
    Greenland S
    J Clin Epidemiol; 2021 Nov; 139():264-268. PubMed ID: 34119647
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Comparing the performance of two-stage residual inclusion methods when using physician's prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility.
    Zhang L; Lewsey J
    J Comp Eff Res; 2024 May; 13(5):e230085. PubMed ID: 38567965
    [No Abstract]   [Full Text] [Related]  

  • 6. Misspecification of confounder-exposure and confounder-outcome associations leads to bias in effect estimates.
    Schuster NA; Rijnhart JJM; Bosman LC; Twisk JWR; Klausch T; Heymans MW
    BMC Med Res Methodol; 2023 Jan; 23(1):11. PubMed ID: 36635655
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders.
    Cepeda MS; Boston R; Farrar JT; Strom BL
    Am J Epidemiol; 2003 Aug; 158(3):280-7. PubMed ID: 12882951
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Noncollapsibility, confounding, and sparse-data bias. Part 1: The oddities of odds.
    Greenland S
    J Clin Epidemiol; 2021 Oct; 138():178-181. PubMed ID: 34119646
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Quantitative Bias Analysis for a Misclassified Confounder: A Comparison Between Marginal Structural Models and Conditional Models for Point Treatments.
    Nab L; Groenwold RHH; van Smeden M; Keogh RH
    Epidemiology; 2020 Nov; 31(6):796-805. PubMed ID: 32826524
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study.
    Young JG; Vatsa R; Murray EJ; Hernán MA
    Trials; 2019 Sep; 20(1):552. PubMed ID: 31488202
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The impact of unmeasured baseline effect modification on estimates from an inverse probability of treatment weighted logistic model.
    Delaney JA; Platt RW; Suissa S
    Eur J Epidemiol; 2009; 24(7):343-9. PubMed ID: 19418232
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Propensity Score Weighting and Trimming Strategies for Reducing Variance and Bias of Treatment Effect Estimates: A Simulation Study.
    Stürmer T; Webster-Clark M; Lund JL; Wyss R; Ellis AR; Lunt M; Rothman KJ; Glynn RJ
    Am J Epidemiol; 2021 Aug; 190(8):1659-1670. PubMed ID: 33615349
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.
    Schmidt AF; Klungel OH; Groenwold RH;
    Epidemiology; 2016 Jan; 27(1):133-42. PubMed ID: 26436519
    [TBL] [Abstract][Full Text] [Related]  

  • 15. On the use and misuse of scalar scores of confounders in design and analysis of observational studies.
    Pfeiffer RM; Riedl R
    Stat Med; 2015 Aug; 34(18):2618-35. PubMed ID: 25781579
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Confounding adjustment performance of ordinal analysis methods in stroke studies.
    Zonneveld TP; Aigner A; Groenwold RHH; Algra A; Nederkoorn PJ; Grittner U; Kruyt ND; Siegerink B
    PLoS One; 2020; 15(4):e0231670. PubMed ID: 32298347
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Constructing inverse probability weights for marginal structural models.
    Cole SR; Hernán MA
    Am J Epidemiol; 2008 Sep; 168(6):656-64. PubMed ID: 18682488
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Assessment of the E-value in the presence of bias amplification: a simulation study.
    Barrette E; Higuera L; Wherry K
    BMC Med Res Methodol; 2024 Mar; 24(1):79. PubMed ID: 38539082
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies.
    Kubota K; Kelly TL; Sato T; Pratt N; Roughead E; Yamaguchi T
    BMC Med Res Methodol; 2021 Oct; 21(1):214. PubMed ID: 34657592
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.