Literature DB >> 8134734

Cross-validation in survival analysis.

P J Verweij1, H C Van Houwelingen.   

Abstract

The predictive value of a statistical model is conceptually different from the explained variation. In this paper we construct a measure of the predictive value of the Cox proportional hazards model, computed from the leave-one-out regression coefficients. These coefficients can also be used to calculate a shrinkage factor which can be applied to improve the predictions and that can be used in R2-type measures of the proportion of explained variation. Our methods are illustrated by a study of chemotherapy for advanced ovarian cancer.

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Year:  1993        PMID： 8134734     DOI： 10.1002/sim.4780122407

Source DB:  PubMed          Journal:  Stat Med        ISSN： 0277-6715            Impact factor:   2.373


  57 in total

1.  Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure.

Authors:  Xiaonan Xue; Mimi Y Kim; Roy E Shore
Journal:  Lifetime Data Anal       Date:  2007-07-28       Impact factor: 1.588

2.  Partial least squares Cox regression for genome-wide data.

Authors:  Ståle Nygård; Ornulf Borgan; Ole Christian Lingjaerde; Hege Leite Størvold
Journal:  Lifetime Data Anal       Date:  2008-06       Impact factor: 1.588

3.  Patient-centered yes/no prognosis using learning machines.

Authors:  I R König; J D Malley; S Pajevic; C Weimar; H-C Diener; A Ziegler
Journal:  Int J Data Min Bioinform       Date:  2008       Impact factor: 0.667

4.  The additive hazards model with high-dimensional regressors.

Authors:  Torben Martinussen; Thomas H Scheike
Journal:  Lifetime Data Anal       Date:  2009-01-28       Impact factor: 1.588

5.  Quantifying cardiometabolic risk using modifiable non-self-reported risk factors.

Authors:  Miguel Marino; Yi Li; Michael J Pencina; Ralph B D'Agostino; Lisa F Berkman; Orfeu M Buxton
Journal:  Am J Prev Med       Date:  2014-06-17       Impact factor: 5.043

6.  survcomp: an R/Bioconductor package for performance assessment and comparison of survival models.

Authors:  Markus S Schröder; Aedín C Culhane; John Quackenbush; Benjamin Haibe-Kains
Journal:  Bioinformatics       Date:  2011-09-07       Impact factor: 6.937

7.  Boosting multi-state models.

Authors:  Holger Reulen; Thomas Kneib
Journal:  Lifetime Data Anal       Date:  2015-05-20       Impact factor: 1.588

8.  Combined analysis of O6-methylguanine-DNA methyltransferase protein expression and promoter methylation provides optimized prognostication of glioblastoma outcome.

Authors:  Shadi Lalezari; Arthur P Chou; Anh Tran; Orestes E Solis; Negar Khanlou; Weidong Chen; Sichen Li; Jose A Carrillo; Reshmi Chowdhury; Julia Selfridge; Desiree E Sanchez; Ryan W Wilson; Mira Zurayk; Jonathan Lalezari; Jerry J Lou; Laurel Ormiston; Karen Ancheta; Robert Hanna; Paul Miller; David Piccioni; Benjamin M Ellingson; Colin Buchanan; Paul S Mischel; Phioanh L Nghiemphu; Richard Green; He-Jing Wang; Whitney B Pope; Linda M Liau; Robert M Elashoff; Timothy F Cloughesy; William H Yong; Albert Lai
Journal:  Neuro Oncol       Date:  2013-01-17       Impact factor: 12.300

9.  Human TERT promoter mutation enables survival advantage from MGMT promoter methylation in IDH1 wild-type primary glioblastoma treated by standard chemoradiotherapy.

Authors:  HuyTram N Nguyen; Amy Lie; Tie Li; Reshmi Chowdhury; Fei Liu; Byram Ozer; Bowen Wei; Richard M Green; Benjamin M Ellingson; He-Jing Wang; Robert Elashoff; Linda M Liau; William H Yong; Phioanh L Nghiemphu; Timothy Cloughesy; Albert Lai
Journal:  Neuro Oncol       Date:  2017-03-01       Impact factor: 12.300

10.  Survival prediction from clinico-genomic models--a comparative study.

Authors:  Hege M Bøvelstad; Ståle Nygård; Ornulf Borgan
Journal:  BMC Bioinformatics       Date:  2009-12-13       Impact factor: 3.169
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