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 *

188 related articles for article (PubMed ID: 19021241)

  • 41. Analysis of time-dependent covariates in a regressive relative survival model.
    Giorgi R; Gouvernet J
    Stat Med; 2005 Dec; 24(24):3863-70. PubMed ID: 16320266
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Emergency presentation of colorectal cancer is associated with poor 5-year survival.
    McArdle CS; Hole DJ
    Br J Surg; 2004 May; 91(5):605-9. PubMed ID: 15122613
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Prognostic variables and prognostic groups for malignant melanoma. The information from Cox and Classification And Regression Trees analysis: an Italian population-based study.
    Crocetti E; Mangone L; Lo Scocco G; Carli P
    Melanoma Res; 2006 Oct; 16(5):429-33. PubMed ID: 17013092
    [TBL] [Abstract][Full Text] [Related]  

  • 44. Estimating net survival: the importance of allowing for informative censoring.
    Danieli C; Remontet L; Bossard N; Roche L; Belot A
    Stat Med; 2012 Apr; 31(8):775-86. PubMed ID: 22281942
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Imputation of missing values of tumour stage in population-based cancer registration.
    Eisemann N; Waldmann A; Katalinic A
    BMC Med Res Methodol; 2011 Sep; 11():129. PubMed ID: 21929796
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Dealing with missing outcome data in randomized trials and observational studies.
    Groenwold RH; Donders AR; Roes KC; Harrell FE; Moons KG
    Am J Epidemiol; 2012 Feb; 175(3):210-7. PubMed ID: 22262640
    [TBL] [Abstract][Full Text] [Related]  

  • 47. Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study.
    Marshall A; Altman DG; Holder RL
    BMC Med Res Methodol; 2010 Dec; 10():112. PubMed ID: 21194416
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Multiple imputation methods for inference on cumulative incidence with missing cause of failure.
    Lee M; Cronin KA; Gail MH; Dignam JJ; Feuer EJ
    Biom J; 2011 Nov; 53(6):974-93. PubMed ID: 22028204
    [TBL] [Abstract][Full Text] [Related]  

  • 49. The impact of additional life-table variables on excess mortality estimates.
    Grafféo N; Jooste V; Giorgi R
    Stat Med; 2012 Dec; 31(30):4219-30. PubMed ID: 22806957
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Predictors of clinical outcome in pediatric oligodendroglioma: meta-analysis of individual patient data and multiple imputation.
    Wang KY; Vankov ER; Lin DDM
    J Neurosurg Pediatr; 2018 Feb; 21(2):153-163. PubMed ID: 29192869
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example.
    Knol MJ; Janssen KJ; Donders AR; Egberts AC; Heerdink ER; Grobbee DE; Moons KG; Geerlings MI
    J Clin Epidemiol; 2010 Jul; 63(7):728-36. PubMed ID: 20346625
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Relative survival multistate Markov model.
    Huszti E; Abrahamowicz M; Alioum A; Binquet C; Quantin C
    Stat Med; 2012 Feb; 31(3):269-86. PubMed ID: 22052528
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Missing data imputation using statistical and machine learning methods in a real breast cancer problem.
    Jerez JM; Molina I; García-Laencina PJ; Alba E; Ribelles N; Martín M; Franco L
    Artif Intell Med; 2010 Oct; 50(2):105-15. PubMed ID: 20638252
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Missing not at random models for latent growth curve analyses.
    Enders CK
    Psychol Methods; 2011 Mar; 16(1):1-16. PubMed ID: 21381816
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.
    Marshall A; Altman DG; Royston P; Holder RL
    BMC Med Res Methodol; 2010 Jan; 10():7. PubMed ID: 20085642
    [TBL] [Abstract][Full Text] [Related]  

  • 56. Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings.
    Donneau AF; Mauer M; Lambert P; Molenberghs G; Albert A
    J Biopharm Stat; 2015; 25(3):570-601. PubMed ID: 24905056
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Correcting bias due to missing stage data in the non-parametric estimation of stage-specific net survival for colorectal cancer using multiple imputation.
    Falcaro M; Carpenter JR
    Cancer Epidemiol; 2017 Jun; 48():16-21. PubMed ID: 28315607
    [TBL] [Abstract][Full Text] [Related]  

  • 58. The impact of dropouts on the analysis of dose-finding studies with recurrent event data.
    Akacha M; Benda N
    Stat Med; 2010 Jul; 29(15):1635-46. PubMed ID: 20552569
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Multiple imputation with missing data indicators.
    Beesley LJ; Bondarenko I; Elliot MR; Kurian AW; Katz SJ; Taylor JM
    Stat Methods Med Res; 2021 Dec; 30(12):2685-2700. PubMed ID: 34643465
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Description of an approach based on maximum likelihood to adjust an excess hazard model with a random effect.
    Dupont C; Bossard N; Remontet L; Belot A
    Cancer Epidemiol; 2013 Aug; 37(4):449-56. PubMed ID: 23628129
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.