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|