Predicting Degree of Benefit From Adjuvant Trastuzumab in NSABP Trial B-31, JNCI: Journal of the National Cancer Institute, vol.105, issue.23 ,
DOI : 10.1093/jnci/djt321
Genomic Analysis Reveals That Immune Function Genes Are Strongly Linked to Clinical Outcome in the North Central Cancer Treatment Group N9831 Adjuvant Trastuzumab Trial, Journal of Clinical Oncology, vol.33, issue.7, pp.701-709, 2015. ,
DOI : 10.1200/JCO.2014.57.6298
Developing and Validating Continuous Genomic Signatures in Randomized Clinical Trials for Predictive Medicine, Clinical Cancer Research, vol.18, issue.21, pp.6065-73, 2012. ,
DOI : 10.1158/1078-0432.CCR-12-1206
A visualization method measuring the performance of biomarkers for guiding treatment decisions, Pharmaceutical Statistics, vol.24, issue.2, pp.152-64, 2016. ,
DOI : 10.1002/pst.1728
Inference for survival prediction under the regularized Cox model, Biostatistics, vol.17, issue.4, pp.692-707, 2016. ,
DOI : 10.1093/biostatistics/kxw016
A simple method for deriving the confidence regions for the penalized Cox???s model via the minimand perturbation, Communications in Statistics - Theory and Methods, vol.22, issue.10, 2016. ,
DOI : 10.1214/08-AOS625
Subgroup analysis in randomised controlled trials: importance, indications, and interpretation, The Lancet, vol.365, issue.9454, pp.176-86, 2005. ,
DOI : 10.1016/S0140-6736(05)17709-5
Prognosis - what does the clinician associate with this notion?, Statistics in Medicine, vol.90, issue.4, pp.425-455, 2000. ,
DOI : 10.1002/(SICI)1097-0258(20000229)19:4<425::AID-SIM347>3.0.CO;2-J
Regression models and life-tables (with discussion), J R Stat Soc Ser B, vol.34, pp.187-220, 1972. ,
DOI : 10.1007/978-1-4612-4380-9_37
Identification of biomarker-bytreatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces, Biometrical J ,
Regression shrinkage and selection via the lasso, J R Stat Soc Ser B, vol.58, pp.267-88, 1996. ,
DOI : 10.1111/j.1467-9868.2011.00771.x
The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, pp.1418-1447, 2006. ,
DOI : 10.1198/016214506000000735
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.649.404
Adaptive Lasso for Cox's proportional hazards model, Biometrika, vol.94, issue.3, pp.691-703, 2007. ,
DOI : 10.1093/biomet/asm037
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.7869
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-60, 2001. ,
DOI : 10.1198/016214501753382273
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.4174
Cross-validation in survival analysis, Statistics in Medicine, vol.80, issue.24, pp.2305-2319, 1993. ,
DOI : 10.1002/sim.4780122407
Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, pp.1-22, 2010. ,
DOI : 10.18637/jss.v033.i01
URL : http://doi.org/10.18637/jss.v033.i01
Lasso and Elastic-Net Regularized Generalized Linear Models. R-package version 2.0-5. 2016. https://cran.r-project.org/package=glmnet, 2016. ,
MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS, Statistics in Medicine, vol.15, issue.4, pp.361-87, 1996. ,
DOI : 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data, Briefings in Bioinformatics, vol.12, issue.3, pp.203-217, 2011. ,
DOI : 10.1093/bib/bbr001
Contribution to the discussion of the paper by DR Cox, J R Stat Soc Ser B, vol.34, pp.216-223, 1972. ,
Modeling survival data: extending the Cox model, 2000. ,
DOI : 10.1007/978-1-4757-3294-8
Covariance Analysis of Censored Survival Data, Biometrics, vol.30, issue.1, pp.89-99, 1974. ,
DOI : 10.2307/2529620
survival: Survival Analysis. R-package version 2.40-1. 2016. https://cran.r-project.org/package=survival, 2016. ,
A fast and efficient implementation of qualitatively constrained quantile smoothing splines, Statistical Modelling, vol.7, issue.4, pp.315-343, 2007. ,
DOI : 10.1177/1471082X0700700403
Qualitatively Constrained (Regression) Smoothing Splines via Linear Programming and Sparse Matrices. R-package version 1.3-1. 2015. https://cran.r-project.org/package=cobs, 2016. ,
Note on Grouping, Journal of the American Statistical Association, vol.13, issue.280, pp.543-550, 1957. ,
DOI : 10.1214/aoms/1177730881
Dichotomizing continuous predictors in multiple regression: a bad idea, Statistics in Medicine, vol.22, issue.1, pp.127-141, 2006. ,
DOI : 10.1002/sim.2331
Shrinkage-based Diagonal Discriminant Analysis and Its Applications in High-Dimensional Data, Biometrics, vol.109, issue.4, pp.1021-1030, 2009. ,
DOI : 10.1111/j.1541-0420.2009.01200.x
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2794982
Assessment and comparison of prognostic classification schemes for survival data, Statistics in Medicine, vol.307, issue.17-18, pp.2529-2574, 1999. ,
DOI : 10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5
On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data, Statistics in Medicine, vol.81, issue.2, pp.1105-1122, 2011. ,
DOI : 10.1002/sim.4154
SOX4 overexpression is a novel biomarker of malignant status and poor prognosis in breast cancer patients, Tumor Biology, vol.121, issue.6, pp.4167-73, 2015. ,
DOI : 10.1007/s13277-015-3051-9
Breast Cancer Molecular Signatures as Determined by SAGE: Correlation with Lymph Node Status, Molecular Cancer Research, vol.5, issue.9, pp.881-90, 2007. ,
DOI : 10.1158/1541-7786.MCR-07-0055
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4186709
Molecular Pathways: Involvement of Immune Pathways in the Therapeutic Response and Outcome in Breast Cancer, Clinical Cancer Research, vol.19, issue.1, pp.28-33, 2013. ,
DOI : 10.1158/1078-0432.CCR-11-2701
Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial, Annals of Oncology, vol.25, issue.8, pp.1544-50, 2014. ,
DOI : 10.1093/annonc/mdu112
Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models, Statistics in Medicine, vol.96, issue.456, pp.2561-73, 2016. ,
DOI : 10.1002/sim.6927
Personalized medicine: risk prediction, targeted therapies and mobile health technology, BMC Medicine, vol.8, issue.1, p.37, 2014. ,
DOI : 10.1136/bmj.39255.669444.AE
URL : http://doi.org/10.1186/1741-7015-12-37
Prediction error estimation: a comparison of resampling methods, Bioinformatics, vol.21, issue.15, pp.3301-3308, 2005. ,
DOI : 10.1093/bioinformatics/bti499
Survival prediction from clinico-genomic models - a comparative study, BMC Bioinformatics, vol.10, issue.1, p.413, 2009. ,
DOI : 10.1186/1471-2105-10-413
Investigating the prediction ability of survival models based on both clinical and omics data: two case studies, Statistics in Medicine, vol.65, issue.30, pp.5310-5339, 2014. ,
DOI : 10.1002/sim.6246