Abstract
Clinical characterization of breast cancer patients related to their risk and profiles is an important part for making their correct prognostic assessments. This paper first proposes a prognostic index obtained when it is applied a flexible non-linear time-to-event model and compares it to a widely used linear survival estimator. This index underpins different stratification methodologies including informed clustering utilising the principle of learning metrics, regression trees and recursive application of the log-rank test. Missing data issue was overcome using multiple imputation, which was applied to a neural network model of survival fitted to a data set for breast cancer (n=743). It was found the three methodologies broadly agree, having however important differences.
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Fernandes, A.S., Bacciu, D., Jarman, I.H., Etchells, T.A., Fonseca, J.M., Lisboa, P.J.G. (2010). Different Methodologies for Patient Stratification Using Survival Data. In: Masulli, F., Peterson, L.E., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2009. Lecture Notes in Computer Science(), vol 6160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14571-1_21
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DOI: https://doi.org/10.1007/978-3-642-14571-1_21
Publisher Name: Springer, Berlin, Heidelberg
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