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 *

239 related articles for article (PubMed ID: 8961465)

  • 1. Assessing interactions of binary time-dependent covariates with time in cox proportional hazards regression models using cubic spline functions.
    Heinzl H; Kaider A; Zlabinger G
    Stat Med; 1996 Dec; 15(23):2589-601. PubMed ID: 8961465
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Assessing time-by-covariate interactions in relative survival models using restrictive cubic spline functions.
    Bolard P; Quantin C; Abrahamowicz M; Esteve J; Giorgi R; Chadha-Boreham H; Binquet C; Faivre J
    J Cancer Epidemiol Prev; 2002; 7(3):113-22. PubMed ID: 12665210
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Gaining more flexibility in Cox proportional hazards regression models with cubic spline functions.
    Heinzl H; Kaider A
    Comput Methods Programs Biomed; 1997 Nov; 54(3):201-8. PubMed ID: 9421665
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SAS macros for estimation of the cumulative incidence functions based on a Cox regression model for competing risks survival data.
    Rosthøj S; Andersen PK; Abildstrom SZ
    Comput Methods Programs Biomed; 2004 Apr; 74(1):69-75. PubMed ID: 14992828
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A set of SAS macros for calculating and displaying adjusted odds ratios (with confidence intervals) for continuous covariates in logistic B-spline regression models.
    Gregory M; Ulmer H; Pfeiffer KP; Lang S; Strasak AM
    Comput Methods Programs Biomed; 2008 Oct; 92(1):109-14. PubMed ID: 18603325
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A relative survival regression model using B-spline functions to model non-proportional hazards.
    Giorgi R; Abrahamowicz M; Quantin C; Bolard P; Esteve J; Gouvernet J; Faivre J
    Stat Med; 2003 Sep; 22(17):2767-84. PubMed ID: 12939785
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Fitting semiparametric additive hazards models using standard statistical software.
    Schaubel DE; Wei G
    Biom J; 2007 Aug; 49(5):719-30. PubMed ID: 17638295
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Estimation of predictive accuracy in survival analysis using R and S-PLUS.
    Lusa L; Miceli R; Mariani L
    Comput Methods Programs Biomed; 2007 Aug; 87(2):132-7. PubMed ID: 17601627
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Reduced-rank hazard regression for modelling non-proportional hazards.
    Perperoglou A; le Cessie S; van Houwelingen HC
    Stat Med; 2006 Aug; 25(16):2831-45. PubMed ID: 16158396
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Time-dependent covariates in the proportional subdistribution hazards model for competing risks.
    Beyersmann J; Schumacher M
    Biostatistics; 2008 Oct; 9(4):765-76. PubMed ID: 18434297
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Attenuation caused by infrequently updated covariates in survival analysis.
    Andersen PK; Liestøl K
    Biostatistics; 2003 Oct; 4(4):633-49. PubMed ID: 14557116
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Tests for treatment group differences in the hazards for survival, before and after the occurrence of an intermediate event.
    Bebchuk JD; Betensky RA
    Stat Med; 2005 Feb; 24(3):359-78. PubMed ID: 15568187
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predictive survival model with time-dependent prognostic factors: development of computer-aided SAS Macro program.
    Chen LS; Yen MF; Wu HM; Liao CS; Liou DM; Kuo HS; Chen TH
    J Eval Clin Pract; 2005 Apr; 11(2):181-93. PubMed ID: 15813715
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Partly conditional survival models for longitudinal data.
    Zheng Y; Heagerty PJ
    Biometrics; 2005 Jun; 61(2):379-91. PubMed ID: 16011684
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Avoiding infinite estimates of time-dependent effects in small-sample survival studies.
    Heinze G; Dunkler D
    Stat Med; 2008 Dec; 27(30):6455-69. PubMed ID: 18816502
    [TBL] [Abstract][Full Text] [Related]  

  • 16. RSURV: a function to perform relative survival analysis with S-PLUS or R.
    Giorgi R; Payan J; Gouvernet J
    Comput Methods Programs Biomed; 2005 May; 78(2):175-8. PubMed ID: 15848272
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Adaptive trial design: a general methodology for censored time to event data.
    Jahn-Eimermacher A; Ingel K
    Contemp Clin Trials; 2009 Mar; 30(2):171-7. PubMed ID: 19130902
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Validity and efficiency of approximation methods for tied survival times in Cox regression.
    Hertz-Picciotto I; Rockhill B
    Biometrics; 1997 Sep; 53(3):1151-6. PubMed ID: 9333345
    [TBL] [Abstract][Full Text] [Related]  

  • 19. The estimation of average hazard ratios by weighted Cox regression.
    Schemper M; Wakounig S; Heinze G
    Stat Med; 2009 Aug; 28(19):2473-89. PubMed ID: 19472308
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Joint models for efficient estimation in proportional hazards regression models.
    Slasor P; Laird N
    Stat Med; 2003 Jul; 22(13):2137-48. PubMed ID: 12820279
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.