BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

121 related articles for article (PubMed ID: 26576494)

  • 1. Gaining power and precision by using model-based weights in the analysis of late stage cancer trials with substantial treatment switching.
    Bowden J; Seaman S; Huang X; White IR
    Stat Med; 2016 Apr; 35(9):1423-40. PubMed ID: 26576494
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A simulation study comparing the power of nine tests of the treatment effect in randomized controlled trials with a time-to-event outcome.
    Royston P; B Parmar MK
    Trials; 2020 Apr; 21(1):315. PubMed ID: 32252820
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study.
    Latimer NR; Abrams KR; Lambert PC; Morden JP; Crowther MJ
    Stat Methods Med Res; 2018 Mar; 27(3):765-784. PubMed ID: 27114326
    [TBL] [Abstract][Full Text] [Related]  

  • 4. rpsftm: An R Package for Rank Preserving Structural Failure Time Models.
    Allison A; White IR; Bond S
    R J; 2017 Dec; 9(2):342-353. PubMed ID: 29564164
    [TBL] [Abstract][Full Text] [Related]  

  • 5. An approach to trial design and analysis in the era of non-proportional hazards of the treatment effect.
    Royston P; Parmar MK
    Trials; 2014 Aug; 15():314. PubMed ID: 25098243
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?
    Latimer NR; White IR; Abrams KR; Siebert U
    Stat Methods Med Res; 2019 Aug; 28(8):2475-2493. PubMed ID: 29940824
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A causal proportional hazards estimator for the effect of treatment actually received in a randomized trial with all-or-nothing compliance.
    Loeys T; Goetghebeur E
    Biometrics; 2003 Mar; 59(1):100-5. PubMed ID: 12762446
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Adjusting survival time estimates to account for treatment switching in randomized controlled trials--an economic evaluation context: methods, limitations, and recommendations.
    Latimer NR; Abrams KR; Lambert PC; Crowther MJ; Wailoo AJ; Morden JP; Akehurst RL; Campbell MJ
    Med Decis Making; 2014 Apr; 34(3):387-402. PubMed ID: 24449433
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A flexible and coherent test/estimation procedure based on restricted mean survival times for censored time-to-event data in randomized clinical trials.
    Horiguchi M; Cronin AM; Takeuchi M; Uno H
    Stat Med; 2018 Jul; 37(15):2307-2320. PubMed ID: 29682762
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study.
    Morden JP; Lambert PC; Latimer N; Abrams KR; Wailoo AJ
    BMC Med Res Methodol; 2011 Jan; 11():4. PubMed ID: 21223539
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Adjusting for treatment switching in randomised controlled trials - A simulation study and a simplified two-stage method.
    Latimer NR; Abrams KR; Lambert PC; Crowther MJ; Wailoo AJ; Morden JP; Akehurst RL; Campbell MJ
    Stat Methods Med Res; 2017 Apr; 26(2):724-751. PubMed ID: 25416688
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Weighted logrank tests for interval censored data when assessment times depend on treatment.
    Fay MP; Shih JH
    Stat Med; 2012 Dec; 31(28):3760-72. PubMed ID: 22786795
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The type I error and power of non-parametric logrank and Wilcoxon tests with adjustment for covariates--a simulation study.
    Jiang H; Symanowski J; Paul S; Qu Y; Zagar A; Hong S
    Stat Med; 2008 Dec; 27(28):5850-60. PubMed ID: 18759373
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Two-stage estimation to adjust for treatment switching in randomised trials: a simulation study investigating the use of inverse probability weighting instead of re-censoring.
    Latimer NR; Abrams KR; Siebert U
    BMC Med Res Methodol; 2019 Mar; 19(1):69. PubMed ID: 30935369
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Hazard ratio inference in stratified clinical trials with time-to-event endpoints and limited sample size.
    Xu R; Mehrotra DV; Shaw PA
    Pharm Stat; 2019 May; 18(3):366-376. PubMed ID: 30706642
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Delayed treatment effects, treatment switching and heterogeneous patient populations: How to design and analyze RCTs in oncology.
    Ristl R; Ballarini NM; Götte H; Schüler A; Posch M; König F
    Pharm Stat; 2021 Jan; 20(1):129-145. PubMed ID: 32830428
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A Validation Study of the Rank-Preserving Structural Failure Time Model: Confidence Intervals and Unique, Multiple, and Erroneous Solutions.
    Ouwens M; Hauch O; Franzén S
    Med Decis Making; 2018 May; 38(4):509-519. PubMed ID: 29607730
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Performance of statistical methods for analysing survival data in the presence of non-random compliance.
    Odondi L; McNamee R
    Stat Med; 2010 Dec; 29(29):2994-3003. PubMed ID: 20963732
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Introducing a new estimator and test for the weighted all-cause hazard ratio.
    Ozga AK; Rauch G
    BMC Med Res Methodol; 2019 Jun; 19(1):118. PubMed ID: 31185922
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Causal inference methods to assess safety upper bounds in randomized trials with noncompliance.
    Wang Y; Berlin JA; Pinheiro J; Wilcox MA
    Clin Trials; 2015 Jun; 12(3):265-75. PubMed ID: 25733675
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.