Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection
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[1] H. Binder,et al. Extending Statistical Boosting , 2014, Methods of Information in Medicine.
[2] J. Bergh,et al. Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series , 2007, Clinical Cancer Research.
[3] Torsten Hothorn,et al. Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression , 2011 .
[4] Thomas A Gerds,et al. Estimating a time‐dependent concordance index for survival prediction models with covariate dependent censoring , 2013, Statistics in medicine.
[5] Matthias Schmid,et al. A comparison of estimators to evaluate the discriminatory power of time‐to‐event models , 2012, Statistics in medicine.
[6] J. Goeman. L1 Penalized Estimation in the Cox Proportional Hazards Model , 2009, Biometrical journal. Biometrische Zeitschrift.
[7] Peter Buhlmann,et al. BOOSTING ALGORITHMS: REGULARIZATION, PREDICTION AND MODEL FITTING , 2007, 0804.2752.
[8] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[9] Xiao Song,et al. A semiparametric approach for the covariate specific ROC curve with survival outcome , 2008 .
[10] Torsten Hothorn,et al. Stability Selection with Error Control , 2015 .
[11] F. Harrell,et al. Regression modelling strategies for improved prognostic prediction. , 1984, Statistics in medicine.
[12] James M. Robins,et al. Unified Methods for Censored Longitudinal Data and Causality , 2003 .
[13] M. Gonen,et al. Concordance probability and discriminatory power in proportional hazards regression , 2005 .
[14] M. Schmid,et al. The Importance of Knowing When to Stop , 2012, Methods of Information in Medicine.
[15] K. Chou,et al. Using LogitBoost classifier to predict protein structural classes. , 2006, Journal of theoretical biology.
[16] Xiaohui Xie,et al. A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index , 2013, Comput. Math. Methods Medicine.
[17] I. Langner. Survival Analysis: Techniques for Censored and Truncated Data , 2006 .
[18] Qi Long,et al. Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic , 2016, Biometrics.
[19] Benjamin Hofner,et al. Model-based boosting in R: a hands-on tutorial using the R package mboost , 2012, Computational Statistics.
[20] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[21] P. Heagerty,et al. Survival Model Predictive Accuracy and ROC Curves , 2005, Biometrics.
[22] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[23] Ralph B. D'Agostino,et al. Evaluation of the Performance of Survival Analysis Models: Discrimination and Calibration Measures , 2003, Advances in Survival Analysis.
[24] Elia Biganzoli,et al. A time‐dependent discrimination index for survival data , 2005, Statistics in medicine.
[25] P. Bühlmann,et al. Boosting With the L2 Loss , 2003 .
[26] Matthias Schmid,et al. A permutation test to analyse systematic bias and random measurement errors of medical devices via boosting location and scale models , 2017, Statistical methods in medical research.
[27] J. Wyatt,et al. Commentary: Prognostic models: clinically useful or quickly forgotten? , 1995 .
[28] Matthias Schmid,et al. A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error , 2011, Biometrics.
[29] Bernd Bischl,et al. The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models , 2016, Biometrics.
[30] M. Pencina,et al. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation , 2004, Statistics in medicine.
[31] J. Foekens,et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.
[32] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[33] Hans A. Kestler,et al. On the validity of time-dependent AUC estimators , 2015, Briefings Bioinform..
[34] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[35] Benjamin Hofner,et al. Controlling false discoveries in high-dimensional situations: boosting with stability selection , 2014, BMC Bioinformatics.
[36] F. Harrell,et al. Evaluating the yield of medical tests. , 1982, JAMA.
[37] Hemant Ishwaran,et al. Evaluating Random Forests for Survival Analysis using Prediction Error Curves. , 2012, Journal of statistical software.
[38] Hong Liu,et al. A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses , 2015, BMC Bioinformatics.
[39] Michael W Kattan,et al. Evaluating a New Marker’s Predictive Contribution , 2004, Clinical Cancer Research.
[40] P. Bühlmann,et al. Boosting with the L2-loss: regression and classification , 2001 .
[41] M. Pencina,et al. On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data , 2011, Statistics in medicine.
[42] E Graf,et al. Assessment and comparison of prognostic classification schemes for survival data. , 1999, Statistics in medicine.
[43] Torsten Hothorn,et al. Testing the additional predictive value of high-dimensional molecular data , 2010, BMC Bioinformatics.
[44] J. Klein,et al. Survival Analysis: Techniques for Censored and Truncated Data , 1997 .
[45] A Mayr,et al. The Evolution of Boosting Algorithms , 2014, Methods of Information in Medicine.
[46] S. Dudoit,et al. Multiple Hypothesis Testing in Microarray Experiments , 2003 .
[47] Trevor Hastie,et al. Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. , 2011, Journal of statistical software.
[48] Tso-Jung Yen,et al. Discussion on "Stability Selection" by Meinshausen and Buhlmann , 2010 .
[49] Rajen Dinesh Shah,et al. Variable selection with error control: another look at stability selection , 2011, 1105.5578.
[50] Sabine Van Huffel,et al. Support vector methods for survival analysis: a comparison between ranking and regression approaches , 2011, Artif. Intell. Medicine.
[51] Ying Huang,et al. Evaluating the ROC performance of markers for future events , 2008, Lifetime data analysis.
[52] Hemant Ishwaran,et al. Random Survival Forests , 2008, Wiley StatsRef: Statistics Reference Online.
[53] Matthias Schmid,et al. Boosting the Concordance Index for Survival Data – A Unified Framework To Derive and Evaluate Biomarker Combinations , 2013, PloS one.
[54] Jean-Baptiste Veyrieras,et al. A strategy to build and validate a prognostic biomarker model based on RT-qPCR gene expression and clinical covariates , 2015, BMC Bioinformatics.
[55] John T. Kent,et al. Measures of dependence for censored survival data , 1988 .
[56] Jian Huang,et al. Regularized ROC method for disease classification and biomarker selection with microarray data , 2005, Bioinform..
[57] John O'Quigley,et al. Explained randomness in proportional hazards models , 2005, Statistics in medicine.
[58] Torsten Hothorn,et al. A PAUC-based Estimation Technique for Disease Classification and Biomarker Selection , 2012, Statistical applications in genetics and molecular biology.
[59] David Mease,et al. Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers , 2015, J. Mach. Learn. Res..
[60] Harald Binder,et al. A weighting approach for judging the effect of patient strata on high-dimensional risk prediction signatures , 2015, BMC Bioinformatics.
[61] Robert Tibshirani,et al. Survival analysis with high-dimensional covariates , 2010, Statistical methods in medical research.
[62] B. Yu,et al. Boosting with the L_2-Loss: Regression and Classification , 2001 .
[63] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .