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.
|Title of host publication||Computational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes in Computer Science|
|Place of Publication||GERMANY|
|Publication status||Published - 1 Jan 2010|
|Name||Lecture Notes in Computer Science|
Fonseca, J. M. M. R. D., & DEE Group Author (2010). Different Methodologies for Patient Stratification Using Survival Data. In Springer (Ed.), Computational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes in Computer Science (Vol. 6160, pp. 276-290). (Lecture Notes in Computer Science). GERMANY: Springer. https://doi.org/10.1007/978-3-642-14571-1_21