BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

199 related articles for article (PubMed ID: 32658651)

  • 1. A Practical Overview and Reporting Strategies for Statistical Analysis of Survival Studies.
    Dey T; Mukherjee A; Chakraborty S
    Chest; 2020 Jul; 158(1S):S39-S48. PubMed ID: 32658651
    [TBL] [Abstract][Full Text] [Related]  

  • 2. When do we need competing risks methods for survival analysis in nephrology?
    Noordzij M; Leffondré K; van Stralen KJ; Zoccali C; Dekker FW; Jager KJ
    Nephrol Dial Transplant; 2013 Nov; 28(11):2670-7. PubMed ID: 23975843
    [TBL] [Abstract][Full Text] [Related]  

  • 3. The importance of censoring in competing risks analysis of the subdistribution hazard.
    Donoghoe MW; Gebski V
    BMC Med Res Methodol; 2017 Apr; 17(1):52. PubMed ID: 28376736
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Cumulative incidence in competing risks data and competing risks regression analysis.
    Kim HT
    Clin Cancer Res; 2007 Jan; 13(2 Pt 1):559-65. PubMed ID: 17255278
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks.
    Nagpal C; Li X; Dubrawski A
    IEEE J Biomed Health Inform; 2021 Aug; 25(8):3163-3175. PubMed ID: 33460387
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Survival analysis in public health research.
    Lee ET; Go OT
    Annu Rev Public Health; 1997; 18():105-34. PubMed ID: 9143714
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Designing clinical trials with (restricted) mean survival time endpoint: Practical considerations.
    Eaton A; Therneau T; Le-Rademacher J
    Clin Trials; 2020 Jun; 17(3):285-294. PubMed ID: 32063031
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Testing the proportional hazards assumption in cox regression and dealing with possible non-proportionality in total joint arthroplasty research: methodological perspectives and review.
    Kuitunen I; Ponkilainen VT; Uimonen MM; Eskelinen A; Reito A
    BMC Musculoskelet Disord; 2021 May; 22(1):489. PubMed ID: 34049528
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Survival Analysis and Interpretation of Time-to-Event Data: The Tortoise and the Hare.
    Schober P; Vetter TR
    Anesth Analg; 2018 Sep; 127(3):792-798. PubMed ID: 30015653
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The win ratio: Impact of censoring and follow-up time and use with nonproportional hazards.
    Dong G; Huang B; Chang YW; Seifu Y; Song J; Hoaglin DC
    Pharm Stat; 2020 May; 19(3):168-177. PubMed ID: 31671481
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Model selection in competing risks regression.
    Kuk D; Varadhan R
    Stat Med; 2013 Aug; 32(18):3077-88. PubMed ID: 23436643
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A win ratio approach for comparing crossing survival curves in clinical trials.
    Zheng S; Wang D; Qiu J; Chen T; Gamalo M
    J Biopharm Stat; 2023 Jul; 33(4):488-501. PubMed ID: 36749067
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Ignoring competing events in the analysis of survival data may lead to biased results: a nonmathematical illustration of competing risk analysis.
    Schuster NA; Hoogendijk EO; Kok AAL; Twisk JWR; Heymans MW
    J Clin Epidemiol; 2020 Jun; 122():42-48. PubMed ID: 32165133
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Performing Survival Analyses in the Presence of Competing Risks: A Clinical Example in Older Breast Cancer Patients.
    de Glas NA; Kiderlen M; Vandenbroucke JP; de Craen AJ; Portielje JE; van de Velde CJ; Liefers GJ; Bastiaannet E; Le Cessie S
    J Natl Cancer Inst; 2016 May; 108(5):. PubMed ID: 26614095
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Survival studies: competing risks, immortality and censoring.
    Barnett AG; Oldmeadow C; Attia JR
    Med J Aust; 2018 Jun; 208(11):475-477. PubMed ID: 29902402
    [No Abstract]   [Full Text] [Related]  

  • 16. Cause-specific cumulative incidence estimation and the fine and gray model under both left truncation and right censoring.
    Geskus RB
    Biometrics; 2011 Mar; 67(1):39-49. PubMed ID: 20377575
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Time-To-Event Data: An Overview and Analysis Considerations.
    Le-Rademacher J; Wang X
    J Thorac Oncol; 2021 Jul; 16(7):1067-1074. PubMed ID: 33887465
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Planning and analyzing clinical trials with competing risks: Recommendations for choosing appropriate statistical methodology.
    Poythress JC; Lee MY; Young J
    Pharm Stat; 2020 Jan; 19(1):4-21. PubMed ID: 31625290
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Comparison of two treatments in the presence of competing risks.
    Lyu J; Chen J; Hou Y; Chen Z
    Pharm Stat; 2020 Nov; 19(6):746-762. PubMed ID: 32476264
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Practical recommendations for reporting Fine-Gray model analyses for competing risk data.
    Austin PC; Fine JP
    Stat Med; 2017 Nov; 36(27):4391-4400. PubMed ID: 28913837
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.