Abstract
| Original language | Unknown |
|---|---|
| Title of host publication | Computational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes in Computer Science |
| Editors | Springer |
| Place of Publication | GERMANY |
| Publisher | Springer |
| Pages | 276-290 |
| Volume | 6160 |
| ISBN (Print) | 978-3-642-14570-4 |
| DOIs | |
| Publication status | Published - 1 Jan 2010 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
Cite this
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Different Methodologies for Patient Stratification Using Survival Data. / Fonseca, José Manuel Matos Ribeiro da; DEE Group Author.
Computational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes in Computer Science. ed. / Springer. Vol. 6160 GERMANY : Springer, 2010. p. 276-290 (Lecture Notes in Computer Science).Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Different Methodologies for Patient Stratification Using Survival Data
AU - Fonseca, José Manuel Matos Ribeiro da
AU - DEE Group Author
PY - 2010/1/1
Y1 - 2010/1/1
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-642-14571-1_21
DO - 10.1007/978-3-642-14571-1_21
M3 - Chapter
SN - 978-3-642-14570-4
VL - 6160
T3 - Lecture Notes in Computer Science
SP - 276
EP - 290
BT - Computational Intelligence Methods for Bioinformatics and Biostatistics Lecture Notes in Computer Science
A2 - Springer, null
PB - Springer
CY - GERMANY
ER -