Different Methodologies for Patient Stratification Using Survival Data

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

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.
Original languageUnknown
Title of host publicationComputational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes in Computer Science
Editors Springer
Place of PublicationGERMANY
PublisherSpringer
Pages276-290
Volume6160
ISBN (Print)978-3-642-14570-4
DOIs
Publication statusPublished - 1 Jan 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

Cite this

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