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 *

198 related articles for article (PubMed ID: 26929180)

  • 1. Statistical issues in the analysis of adverse events in time-to-event data.
    Allignol A; Beyersmann J; Schmoor C
    Pharm Stat; 2016 Jul; 15(4):297-305. PubMed ID: 26929180
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Survival analysis for AdVerse events with VarYing follow-up times (SAVVY)-estimation of adverse event risks.
    Stegherr R; Schmoor C; Beyersmann J; Rufibach K; Jehl V; Brückner A; Eisele L; Künzel T; Kupas K; Langer F; Leverkus F; Loos A; Norenberg C; Voss F; Friede T
    Trials; 2021 Jun; 22(1):420. PubMed ID: 34187527
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): summary of findings and assessment of existing guidelines.
    Rufibach K; Beyersmann J; Friede T; Schmoor C; Stegherr R
    Trials; 2024 May; 25(1):353. PubMed ID: 38822392
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Safety data from randomized controlled trials: applying models for recurrent events.
    Hengelbrock J; Gillhaus J; Kloss S; Leverkus F
    Pharm Stat; 2016 Jul; 15(4):315-23. PubMed ID: 27291933
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Non-parametric inference of adverse events under informative censoring.
    Nishikawa M; Tango T; Ogawa M
    Stat Med; 2006 Dec; 25(23):3981-4003. PubMed ID: 16526008
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Estimating and comparing adverse event probabilities in the presence of varying follow-up times and competing events.
    Stegherr R; Schmoor C; Lübbert M; Friede T; Beyersmann J
    Pharm Stat; 2021 Nov; 20(6):1125-1146. PubMed ID: 34002935
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Competing time-to-event endpoints in cardiology trials: a simulation study to illustrate the importance of an adequate statistical analysis.
    Rauch G; Kieser M; Ulrich S; Doherty P; Rauch B; Schneider S; Riemer T; Senges J
    Eur J Prev Cardiol; 2014 Jan; 21(1):74-80. PubMed ID: 22964966
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Meta-analysis for aggregated survival data with competing risks: a parametric approach using cumulative incidence functions.
    Bonofiglio F; Beyersmann J; Schumacher M; Koller M; Schwarzer G
    Res Synth Methods; 2016 Sep; 7(3):282-93. PubMed ID: 26387882
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Evaluating health outcomes in the presence of competing risks: a review of statistical methods and clinical applications.
    Varadhan R; Weiss CO; Segal JB; Wu AW; Scharfstein D; Boyd C
    Med Care; 2010 Jun; 48(6 Suppl):S96-105. PubMed ID: 20473207
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Analysing adverse events by time-to-event models: the CLEOPATRA study.
    Proctor T; Schumacher M
    Pharm Stat; 2016 Jul; 15(4):306-14. PubMed ID: 27313144
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Quantitative assessment of adverse events in clinical trials: Comparison of methods at an interim and the final analysis.
    Hollaender N; Gonzalez-Maffe J; Jehl V
    Biom J; 2020 May; 62(3):658-669. PubMed ID: 31756032
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Adverse events in single-arm clinical trials with non-fatal time-to-event efficacy endpoint: from clinical questions to methods for statistical analysis.
    Tassistro E; Bernasconi DP; Valsecchi MG; Antolini L
    BMC Med Res Methodol; 2024 Jan; 24(1):3. PubMed ID: 38172810
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Estimating cumulative incidence functions in competing risks data with dependent left-truncation.
    Stegherr R; Allignol A; Meister R; Schaefer C; Beyersmann J
    Stat Med; 2020 Feb; 39(4):481-493. PubMed ID: 31788835
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Introduction to the Analysis of Survival Data in the Presence of Competing Risks.
    Austin PC; Lee DS; Fine JP
    Circulation; 2016 Feb; 133(6):601-9. PubMed ID: 26858290
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Improved confidence intervals for a difference of two cause-specific cumulative incidence functions estimated in the presence of competing risks and random censoring.
    Scosyrev E
    Biom J; 2020 Oct; 62(6):1394-1407. PubMed ID: 32227361
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of nonparametric estimators of the expected number of recurrent events.
    Erdmann A; Beyersmann J; Bluhmki E
    Pharm Stat; 2024; 23(3):339-369. PubMed ID: 38153191
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Kaplan-Meier Survival Analysis Overestimates the Risk of Revision Arthroplasty: A Meta-analysis.
    Lacny S; Wilson T; Clement F; Roberts DJ; Faris PD; Ghali WA; Marshall DA
    Clin Orthop Relat Res; 2015 Nov; 473(11):3431-42. PubMed ID: 25804881
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A note on variance estimation of the Aalen-Johansen estimator of the cumulative incidence function in competing risks, with a view towards left-truncated data.
    Allignol A; Schumacher M; Beyersmann J
    Biom J; 2010 Feb; 52(1):126-37. PubMed ID: 20140901
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Adverse events associated with bevacizumab and chemotherapy in older patients with metastatic colorectal cancer.
    Shankaran V; Mummy D; Koepl L; Blough D; Yim YM; Yu E; Ramsey S
    Clin Colorectal Cancer; 2013 Sep; 12(3):204-213.e1. PubMed ID: 23276520
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Adaptive and repeated cumulative meta-analyses of safety data during a new drug development process.
    Quan H; Ma Y; Zheng Y; Cho M; Lorenzato C; Hecquet C
    Pharm Stat; 2015; 14(3):161-71. PubMed ID: 25612310
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.