@inbook{d3464f88d703491b9146e65f4cfd2224,
title = "Different Methodologies for Patient Stratification Using Survival Data",
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.",
author = "Fonseca, {Jos{\'e} Manuel Matos Ribeiro da} and {DEE Group Author}",
year = "2010",
month = jan,
day = "1",
doi = "10.1007/978-3-642-14571-1_21",
language = "Unknown",
isbn = "978-3-642-14570-4",
volume = "6160",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "276--290",
editor = "Springer",
booktitle = "Computational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes in Computer Science",
}