Rotation survival forest for right censored data

Recently, survival ensembles have found more and more applications in biological and medical research when censored time-to-event data are often confronted. In this research, we investigate the plausibility of extending a rotation forest, originally proposed for classification purpose, to survival analysis. Supported by the proper statistical analysis, we show that rotation survival forests are able to outperform the state-of-art survival ensembles on right censored data. We also provide a C-index based variable importance measure for evaluating covariates in censored survival data.

[1]  P. Bühlmann,et al.  Survival ensembles. , 2006, Biostatistics.

[2]  Denis Larocque,et al.  A review of survival trees , 2011 .

[3]  Laurence L. George,et al.  The Statistical Analysis of Failure Time Data , 2003, Technometrics.

[4]  Peter F. Thall,et al.  Recent Advances in Clinical Trial Design and Analysis , 1995, Cancer Treatment and Research.

[5]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[6]  Hemant Ishwaran,et al.  Random Survival Forests , 2008, Wiley StatsRef: Statistics Reference Online.

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[9]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[10]  Udaya B. Kogalur,et al.  Consistency of Random Survival Forests. , 2008, Statistics & probability letters.

[11]  A. Benner,et al.  Application of "Aggregated Classifiers" in Survival Time Studies , 2002, COMPSTAT.

[12]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[13]  D Faraggi,et al.  A neural network model for survival data. , 1995, Statistics in medicine.

[14]  M LeBlanc,et al.  A review of tree-based prognostic models. , 1995, Cancer treatment and research.

[15]  F Dannegger,et al.  Tree stability diagnostics and some remedies for instability. , 2000, Statistics in medicine.

[16]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data , 1980 .

[17]  Katharina Burger,et al.  Counting Processes And Survival Analysis , 2016 .

[18]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Kim-Anh Do,et al.  Bayesian ensemble methods for survival prediction in gene expression data , 2011, Bioinform..

[20]  Torsten Hothorn,et al.  Bagging survival trees , 2002, Statistics in medicine.

[21]  H. Heimpel,et al.  Randomized comparison of interferon-alpha with busulfan and hydroxyurea in chronic myelogenous leukemia. The German CML Study Group. , 1994, Blood.

[22]  M. LeBlanc,et al.  Relative risk trees for censored survival data. , 1992, Biometrics.

[23]  Xiaohui Xie,et al.  A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index , 2013, Comput. Math. Methods Medicine.

[24]  R. Kay.  The Analysis of Survival Data , 2012 .

[25]  Wenbin Lu,et al.  Boosting method for nonlinear transformation models with censored survival data. , 2008, Biostatistics.