Different methodologies for patient stratification using survival data

Authors:
Ana S. Fernandes;Davide Bacciu;Ian H. Jarman;Terence A. Etchells;José M. Fonseca;Paulo J. G. Lisboa
Affiliations:
Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa;Dipartimento d'Informatica, Università di Pisa;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK;Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa;School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK
Venue:
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Year:
2009

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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.