The Concordance Index decomposition : A measure for a deeper understanding of survival prediction models
Alabdallah, Abdallah ; Ohlsson, Mattias LU
; Pashami, Sepideh
and Rögnvaldsson, Thorsteinn
(2024)
In Artificial Intelligence in Medicine
148.
- Abstract
The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this... (More)
The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
(Less)
- author
- Alabdallah, Abdallah
; Ohlsson, Mattias
LU
; Pashami, Sepideh
and Rögnvaldsson, Thorsteinn
- organization
- publishing date
- 2024-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Concordance Index, Evaluation metric, Survival analysis, Variational encoder–decoder
- in
- Artificial Intelligence in Medicine
- volume
- 148
- article number
- 102781
- publisher
- Elsevier
- external identifiers
-
- pmid:38325926
- scopus:85184733529
- ISSN
- 0933-3657
- DOI
- 10.1016/j.artmed.2024.102781
- language
- English
- LU publication?
- yes
- id
- 4079daee-f2f2-4ddd-8e56-1a74e5019fd4
- date added to LUP
- 2024-02-22 14:09:42
- date last changed
- 2024-04-22 00:08:24
@article{4079daee-f2f2-4ddd-8e56-1a74e5019fd4,
abstract = {{<p>The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.</p>}},
author = {{Alabdallah, Abdallah and Ohlsson, Mattias and Pashami, Sepideh and Rögnvaldsson, Thorsteinn}},
issn = {{0933-3657}},
keywords = {{Concordance Index; Evaluation metric; Survival analysis; Variational encoder–decoder}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Artificial Intelligence in Medicine}},
title = {{The Concordance Index decomposition : A measure for a deeper understanding of survival prediction models}},
url = {{http://dx.doi.org/10.1016/j.artmed.2024.102781}},
doi = {{10.1016/j.artmed.2024.102781}},
volume = {{148}},
year = {{2024}},
}