BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

172 related articles for article (PubMed ID: 20624484)

  • 1. Estimating adjusted NNTs in randomised controlled trials with binary outcomes: a simulation study.
    Bender R; Vervölgyi V
    Contemp Clin Trials; 2010 Sep; 31(5):498-505. PubMed ID: 20624484
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Estimating adjusted NNT measures in logistic regression analysis.
    Bender R; Kuss O; Hildebrandt M; Gehrmann U
    Stat Med; 2007 Dec; 26(30):5586-95. PubMed ID: 17879268
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Logistic regression was preferred to estimate risk differences and numbers needed to be exposed adjusted for covariates.
    Gehrmann U; Kuss O; Wellmann J; Bender R
    J Clin Epidemiol; 2010 Nov; 63(11):1223-31. PubMed ID: 20430578
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Estimating adjusted risk difference (RD) and number needed to treat (NNT) measures in the Cox regression model.
    Laubender RP; Bender R
    Stat Med; 2010 Mar; 29(7-8):851-9. PubMed ID: 20213710
    [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. Absolute risk reductions, relative risks, relative risk reductions, and numbers needed to treat can be obtained from a logistic regression model.
    Austin PC
    J Clin Epidemiol; 2010 Jan; 63(1):2-6. PubMed ID: 19230611
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements.
    Hernández AV; Steyerberg EW; Habbema JD
    J Clin Epidemiol; 2004 May; 57(5):454-60. PubMed ID: 15196615
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression.
    Localio AR; Margolis DJ; Berlin JA
    J Clin Epidemiol; 2007 Sep; 60(9):874-82. PubMed ID: 17689803
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Complementary nonparametric analysis of covariance for logistic regression in a randomized clinical trial setting.
    Tangen CM; Koch GG
    J Biopharm Stat; 1999 Mar; 9(1):45-66. PubMed ID: 10091909
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Estimators and confidence intervals for the marginal odds ratio using logistic regression and propensity score stratification.
    Stampf S; Graf E; Schmoor C; Schumacher M
    Stat Med; 2010 Mar; 29(7-8):760-9. PubMed ID: 20213703
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Estimation of propensity scores using generalized additive models.
    Woo MJ; Reiter JP; Karr AF
    Stat Med; 2008 Aug; 27(19):3805-16. PubMed ID: 18366144
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The effect of omitted covariates on confidence interval and study power in binary outcome analysis: a simulation study.
    Negassa A; Hanley JA
    Contemp Clin Trials; 2007 May; 28(3):242-8. PubMed ID: 17011835
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Evidence-based dermatology: number needed to treat and its relation to other risk measures.
    Manriquez JJ; Villouta MF; Williams HC
    J Am Acad Dermatol; 2007 Apr; 56(4):664-71. PubMed ID: 17367615
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Longitudinal and repeated cross-sectional cluster-randomization designs using mixed effects regression for binary outcomes: bias and coverage of frequentist and Bayesian methods.
    Localio AR; Berlin JA; Have TR
    Stat Med; 2006 Aug; 25(16):2720-36. PubMed ID: 16345043
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Two-stage instrumental variable methods for estimating the causal odds ratio: analysis of bias.
    Cai B; Small DS; Have TR
    Stat Med; 2011 Jul; 30(15):1809-24. PubMed ID: 21495062
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Common problems related to the use of number needed to treat.
    Stang A; Poole C; Bender R
    J Clin Epidemiol; 2010 Aug; 63(8):820-5. PubMed ID: 19880287
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Use of odds ratio or relative risk to measure a treatment effect in clinical trials with multiple correlated binary outcomes: data from the NINDS t-PA stroke trial.
    Lu M; Tilley BC;
    Stat Med; 2001 Jul; 20(13):1891-901. PubMed ID: 11427947
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Use of the bootstrap in analysing cost data from cluster randomised trials: some simulation results.
    Flynn TN; Peters TJ
    BMC Health Serv Res; 2004 Nov; 4(1):33. PubMed ID: 15550169
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Baseline adjustments for binary data in repeated cross-sectional cluster randomized trials.
    Nixon RM; Thompson SG
    Stat Med; 2003 Sep; 22(17):2673-92. PubMed ID: 12939779
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.