BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

154 related articles for article (PubMed ID: 17623349)

  • 1. A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation.
    Sauerbrei W; Royston P; Look M
    Biom J; 2007 Jun; 49(3):453-73. PubMed ID: 17623349
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Using fractional polynomials and restricted cubic splines to model non-proportional hazards or time-varying covariate effects in the Cox regression model.
    Austin PC; Fang J; Lee DS
    Stat Med; 2022 Feb; 41(3):612-624. PubMed ID: 34806210
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Variables with time-varying effects and the Cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer.
    Bellera CA; MacGrogan G; Debled M; de Lara CT; Brouste V; Mathoulin-Pélissier S
    BMC Med Res Methodol; 2010 Mar; 10():20. PubMed ID: 20233435
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Comparison of procedures to assess non-linear and time-varying effects in multivariable models for survival data.
    Buchholz A; Sauerbrei W
    Biom J; 2011 Mar; 53(2):308-31. PubMed ID: 21328605
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A special case of reduced rank models for identification and modelling of time varying effects in survival analysis.
    Perperoglou A
    Stat Med; 2016 Dec; 35(28):5135-5148. PubMed ID: 27619730
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Effects of influential points and sample size on the selection and replicability of multivariable fractional polynomial models.
    Sauerbrei W; Kipruto E; Balmford J
    Diagn Progn Res; 2023 Apr; 7(1):7. PubMed ID: 37069621
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Building multivariable regression models with continuous covariates in clinical epidemiology--with an emphasis on fractional polynomials.
    Royston P; Sauerbrei W
    Methods Inf Med; 2005; 44(4):561-71. PubMed ID: 16342923
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Joint modelling of repeated transitions in follow-up data--a case study on breast cancer data.
    Genser B; Wernecke KD
    Biom J; 2005 Jun; 47(3):388-401. PubMed ID: 16053262
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Cost-effectiveness of using prognostic information to select women with breast cancer for adjuvant systemic therapy.
    Williams C; Brunskill S; Altman D; Briggs A; Campbell H; Clarke M; Glanville J; Gray A; Harris A; Johnston K; Lodge M
    Health Technol Assess; 2006 Sep; 10(34):iii-iv, ix-xi, 1-204. PubMed ID: 16959170
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A new strategy for meta-analysis of continuous covariates in observational studies.
    Sauerbrei W; Royston P
    Stat Med; 2011 Dec; 30(28):3341-60. PubMed ID: 21953493
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Ascertaining prognosis for breast cancer in node-negative patients with innovative survival analysis.
    Chapman JA; Lickley HL; Trudeau ME; Hanna WM; Kahn HJ; Murray D; Sawka CA; Mobbs BG; McCready DR; Pritchard KI
    Breast J; 2006; 12(1):37-47. PubMed ID: 16409585
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Selection of important variables and determination of functional form for continuous predictors in multivariable model building.
    Sauerbrei W; Royston P; Binder H
    Stat Med; 2007 Dec; 26(30):5512-28. PubMed ID: 18058845
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Comparison of Aalen's additive and Cox proportional hazards models for breast cancer survival: analysis of population- based data from British Columbia, Canada.
    Abadi A; Saadat S; Yavari P; Bajdik C; Jalili P
    Asian Pac J Cancer Prev; 2011; 12(11):3113-6. PubMed ID: 22393999
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The impact of induction duration and the number of high-dose cycles on the long-term survival of women with metastatic breast cancer treated with high-dose chemotherapy with stem cell rescue: an analysis of sequential phase I/II trials from the Dana-Farber/Beth Israel STAMP program.
    Elias AD; Ibrahim J; Richardson P; Avigan D; Joyce R; Reich E; McCauley M; Wheeler C; Frei E
    Biol Blood Marrow Transplant; 2002; 8(4):198-205. PubMed ID: 12017145
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Dynamic Cox modelling based on fractional polynomials: time-variations in gastric cancer prognosis.
    Berger U; Schäfer J; Ulm K
    Stat Med; 2003 Apr; 22(7):1163-80. PubMed ID: 12652560
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Time-dependence of hazard ratios for prognostic factors in primary breast cancer.
    Hilsenbeck SG; Ravdin PM; de Moor CA; Chamness GC; Osborne CK; Clark GM
    Breast Cancer Res Treat; 1998; 52(1-3):227-37. PubMed ID: 10066085
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Thirty-Year Trends of Survival and Time-Varying Effects of Prognostic Factors in Patients With Metastatic Breast Cancer-A Single Institution Experience.
    Rogoz B; Houzé de l'Aulnoit A; Duhamel A; Houzé de l'Aulnoit D
    Clin Breast Cancer; 2018 Jun; 18(3):246-253. PubMed ID: 28988656
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Impact of the model-building strategy on inference about nonlinear and time-dependent covariate effects in survival analysis.
    Wynant W; Abrahamowicz M
    Stat Med; 2014 Aug; 33(19):3318-37. PubMed ID: 24757068
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials.
    Royston P; Sauerbrei W
    Stat Med; 2004 Aug; 23(16):2509-25. PubMed ID: 15287081
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.