Abstract
The heterogeneity of prognoses of patients with apparently the same type of cancer (i.e., same primary site and tumor histology) has long been recognized.
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References
Bair, E., & Tibshirani, R. (2004). Semi-supervised methods to predict patient survival from gene expression data. PLoS Biology, 2, 511–522.
Binder, H., Schumacher M (2008) Allowing for mandatory covariates in boosting estimation of sparse high dimensional survival models. BMC Bioinformatics, 9, 14.
Bovelstad, H. M., & Borgan, O. (2011). Assessment of evaluation criteria for survival prediction from genomic data. Biometrical Journal, 53, 202–216.
Dobbin, K. K., & Simon, R. M. (2011). Optimally splitting cases for training and testing high dimensional classifiers. BMC Medical Genomics, 4, 31.
Freidlin, B., Jiang, W., & Simon, R. (2010). The cross-validated adaptive signature design. Clinical Cancer Research, 16(2), 691–698.
Gerds, T. A., & Schumacher, M. (2006). Consistent estimation of the expected brier score in general survival models with right-censored event times. Biometrical Journal, 48, 1029–1040.
Gönen, M., & Heller, G. (2005). Concordance probability and discriminatory power in proportional hazards regression. Biometrika, 92, 965–970.
Graf, E., Schmoor, C., Sauerbrei, W., & Schumacher, M. (1999). Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine, 18(17–18), 2529–2545.
Hastie, T., & Tibshirani, R. (2004). Efficient quadratic regularization for expression arrays. Biostatistics, 5, 329–340.
Heagerty, P. J., Lumley, T., & Pepe, M. S. (2000). Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics, 56, 337–344.
Hothorn, T., Benner, A., Lausen, B., & Radespiel-Tröger, M. (2004). Bagging survival trees. Statistics in Medicine, 23, 77–91.
Höfling, H., & Tibshirani, R. (2008). A study of pre-validation. The Annals of Applied Statistics, 2, 643–664.
Hofner, B., Mayr, A., Robinzonov, N., & Schmid, M. (2014). Model-based boosting in R: A hands-on tutorial using the R package mboost. Computational Statistics, 29, 3–35.
Hothorn, T., Bühlmann, P., Dudoit, S., Molinaro, A., & Van der Laan, M. (2006). Survival ensembles. Biostatistics, 7, 355–373.
Korn, E. L., & Simon, R. (1990). Measures of explained variation for survival data. Statistics in Medicine, 9, 487–503.
Lai, C., Reinders, M. J., van’t Veer, L. J., & Wessels, L. F. (2006). A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets. BMC Bioinformatics, 7, 235.
Nguyen, D. V., & Rocke, D. M. (2002). Partial least squares proportional hazard regression for application to DNA microarray survival data. Bioinformatics, 18, 1625–1632.
Paik, S., Tang, G., Shak, S., Kim, C., Baker, J., Kim, W., et al. (2006). Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor–positive breast cancer. Journal of Clinical Oncology, 24, 3726–3734.
Park, P. J., Tian, L., & Kohane, I. S. (2002). Linking expression data with patient survival times using partial least squares. Bioinformatics, 18, S120–S127.
Radespiel-Tröger, M., Rabenstein, T., Schneider, H. T., & Lausen, B. (2003). Comparison of tree-based methods for prognostic stratification of survival data. Artifical Intelligence in Medicine, 28, 323–341.
Radmacher, M. D., McShane, L. M., & Simon, R. (2002). A paradigm for class prediction using gene expression profiles. Journal of Computational Biology, 9, 505–511.
Rosenwald, A., Wright, G., Chan, W. C., Connors, J. M., Campo, E., Fisher, R. I., et al. (2002). The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. New England Journal of Medicine, 346, 1937–1947.
Sargent, D. J. (2001). Comparison of artificial neural networks with other statistical approaches. Cancer, 91(S8), 1636–1642.
Segal, M. R. (1998). Regression trees for censored data. Biometrics, 48, 35–47.
Schumacher, M., Graf, E., & Gerds, T. (2003). How to assess prognostic models for survival data: A case study in oncology. Methods Archive, 42, 564–571.
Schumacher, M., Binder, H., & Gerds, T. (2007). Assessment of survival prediction models based on microarray data. Bioinformatics, 23, 1768–1774.
Simon, R. M., Subramanian, J., Li, M. C., & Menezes, S. (2011). Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Briefings in Bioinformatics, 12, 203–214.
Staiger, C., Cadot, S., Györffy, B., Wessels, L. F., & Klau, G. W. (2013). Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis. Frontiers in Genetics, 4.
Subramanian, J., & Simon, R. (2010). Gene expression–based prognostic signatures in lung cancer: Ready for clinical use? Journal of the National Cancer Institute, 102, 464–474.
Tibshirani, R. (1997). The lasso method for variable selection in the Cox model. Statistics in Medicine, 16, 385–395.
Tibshirani, R., Bien, J., Friedman, J., Hastie, T., Simon, N., Taylor, J., et al. (2012). Strong rules for discarding predictors in lasso-type problems. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74, 245–266.
Van Houwelingen, H. C., Bruinsma, T., Hart, A. A. M., Van’t Veer, L. J., & Wessels, L. F. A. (2006). Cross-validated cox regression on microarray gene expression data. Statistics in Medicine, 25, 3201–3216.
van Wieringen, W. N., Kun, D., Hampel, R., & Boulesteix, A. L. (2009). Survival prediction using gene expression data: A review and comparison. Computational Statistics & Data Analysis, 53, 1590–1603.
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Simon, R. (2018). Development of Prognostic Biomarker Signatures for Survival Using High-Dimensional Data. In: Peace, K., Chen, DG., Menon, S. (eds) Biopharmaceutical Applied Statistics Symposium . ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7820-0_16
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