Abstract
As part of the validation of any statistical model, it is good statistical practice to quantify the amount of prognostic information represented by the model; this includes gene expression signatures derived from high-dimensional microarray data. Several approaches exist for right-censored survival data that measure the gain in prognostic information compared to established clinical parameters or biomarkers in terms of explained variation or explained randomness. They are either model-based or use estimates of the prediction accuracy.
As these measures differ in their underlying mechanisms, they vary in their interpretation, assumptions and properties, in particular in how they deal with the presence of censoring. It remains unclear under which conditions and to which extent they are comparable. We present a comparison of several common measures and illustrate their behaviour in simulation examples and in an application to a real gene expression microarray data set.
These authors contributed equally to this work.
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References
Binder, H., & Schumacher, M. (2008). Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples. Statistical Applications in Genetics and Molecular Biology, 7, 12.
Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78, 1–3.
Cox, D. R., & Snell, E. J. (1989). The analysis of binary data (2nd ed.) London: Chapman and Hall.
Dunkler, D., Michiels, S., & Schemper, M. (2007). Gene expression profiling: does it add predictive accuracy to clinical characteristics in cancer prognosis? European Journal of Cancer, 43, 745–751.
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.
Graf, E., Schmoor, C., Sauerbrei, W., & Schumacher, M. (1999). Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine, 18, 2529–2545.
Kent, J. T., & O’Quigley, J. (1988). Measures of dependence for censored survival data. Biometrika, 75, 525–534.
Nagelkerke, J. D. N. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78, 691–692.
O’Quigley, J., Xu, R., & Stare, J. (2005). Explained randomness in proportional hazards models. Statistics in Medicine, 24, 479–489.
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.
Schemper, M., & Henderson, R. (2000). Predictive accuracy and explained variation in cox regression. Biometrics, 56, 249–255.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58, 267–288.
Xu, R., & O’Quigley, J. (1999). A measure of dependence for proportional hazards models. Journal of Nonparametric Statistics, 12, 83–107.
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Hielscher, T., Zucknick, M., Werft, W., Benner, A. (2009). On the Prognostic Value of Gene Expression Signatures for Censored Data. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_61
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DOI: https://doi.org/10.1007/978-3-642-01044-6_61
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